π― What it does: A hierarchical semi-implicit variational inference (HSIVI) method is proposed to construct stronger semi-implicit distributions in multi-layer structures and apply it to high-dimensional Bayesian inference and diffusion model acceleration.
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
Ruiying Lu (Xidian University), Ruimin Hu (Xidian University)
CodeAnomaly DetectionTransformerMixture of ExpertsImage
π― What it does: A Hierarchical Vector Quantization Transformer (HVQ-Trans) for multi-class unsupervised anomaly detection is proposed, achieving efficient localization and detection of anomalies through discrete prototype reconstruction.
π― What it does: A Hierarchical Gated Recurrent Network (HGRN) is proposed, which introduces a learnable lower bound for the forget gate in multi-layer RNNs, allowing lower layers to focus on short-term information while higher layers capture long-term dependencies.
π― What it does: The PLATO method is proposed, which infers the weights of the first layer of a multilayer perceptron (MLP) using an auxiliary knowledge graph that describes features, thereby achieving strong predictive performance in tabular data where the feature dimension is much larger than the sample size (d β« n).
π― What it does: An improved high-fidelity universal neural audio compression algorithm, Improved RVQGAN, is proposed, which compresses 44.1 kHz audio to 8 kbps, achieving approximately 90 times compression;
Homotopy-based training of NeuralODEs for accurate dynamics discovery
Joon-Hyuk Ko (Seoul National University), Wonho Jhe (Seoul National University)
CodeTime SeriesOrdinary Differential Equation
π― What it does: A new training method based on synchronization and homotopy optimization is proposed to improve the training accuracy of Neural Ordinary Differential Equations (NeuralODEs) on long time series data.
How a Student becomes a Teacher: learning and forgetting through Spectral methods
Lorenzo Giambagli (University of Florence), Duccio Fanelli (University of Florence)
CodeOptimizationKnowledge DistillationTabular
π― What it does: A training method for neural networks based on spectral parameterization is proposed, which achieves network learning by directly optimizing the feature vectors and eigenvalues on the weight matrix, and uses spectral regularization to filter the most important nodes.
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model
Michael Hanna (University of Amsterdam), Alexandre Variengien (Redwood Research)
CodeData SynthesisExplainability and InterpretabilityTransformerLarge Language ModelText
π― What it does: This study investigates how GPT-2 small achieves greater-than comparisons in the 'year span prediction' task and locates its internal computational circuits.
π― What it does: This study investigates the effectiveness of resampling methods in long-tail learning and proposes a Context-Shift Augmentation (CSA) module to alleviate irrelevant context overfitting caused by resampling, thereby enhancing the generalization performance of single-stage long-tail learning.
How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
Hai Zhang (Tongji University), Chen Ye (Tongji University)
CodeOptimizationReinforcement Learning
π― What it does: A new model-based reinforcement learning framework called USB-PO is proposed, which unifies the handling of model bias and model drift through model refinement during the training process.
π― What it does: Reinterpret the energy function of mainstream knowledge graph embedding models as probabilistic circuits (GeKC), and achieve a model that is generative, can be trained with maximum likelihood, allows for precise sampling, and meets logical constraints through activation restrictions or squaring.
π― What it does: A multi-agent collaborative perception framework named How2comm is proposed, aiming to significantly reduce communication bandwidth consumption while maintaining perception performance.
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
Han Liu (Dalian University of Technology), Xianchao Zhang (Dalian University of Technology)
CodeAdversarial AttackTransformerText
π― What it does: A high-quality attack framework HQA-Attack is proposed for black-box hard-label text attack scenarios, aiming to generate adversarial texts with high semantic similarity and low perturbation rate under limited query budgets.
π― What it does: A two-stage generative global routing framework called HubRouter is proposed, transforming global routing into a hub-pin connectivity problem.
CodeTransformerLarge Language ModelPrompt EngineeringTextMultimodality
π― What it does: Designed and implemented HuggingGPT, a system that uses a large language model as a controller to automatically plan tasks, select expert models from Hugging Face, execute multimodal tasks, and generate final responses through a language interface;
Human-Aligned Calibration for AI-Assisted Decision Making
Nina L. Corvelo Benz (Max Planck Institute for Software Systems), Manuel Gomez Rodriguez (Max Planck Institute for Software Systems)
CodeText
π― What it does: This paper proposes a theoretical framework regarding the impact of confidence values in AI-assisted decision-making on the behavior of decision-makers. It demonstrates that relying solely on traditional probability calibration may lead decision-makers to make suboptimal choices and introduces the concept of 'human-aligned calibration.' Subsequently, it employs a multicalibration method to achieve this alignment and conducts experimental validation using real human-machine interaction data.
Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
Jacob Granley (University of California), Michael Beyeler (University of California)
CodeOptimizationAuto EncoderImage
π― What it does: A human-computer interaction optimization framework has been developed, utilizing deep encoders and preference Bayesian optimization to achieve personalized stimulation coding for visual prosthetics.
Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language
Kevin Ellis (Cornell University)
CodeExplainability and InterpretabilityMeta LearningTransformerLarge Language ModelText
π― What it does: This paper proposes a few-shot concept learning model based on Bayesian inference, utilizing language models to generate natural language candidate hypotheses and fine-tuning priors with human data, making machine learning closer to human-like rapid and broad concept acquisition.
π― What it does: The HYPO algorithm is proposed to accelerate online learning in sparse reward environments using a small number of low-quality demonstrations.
π― What it does: A pre-trained model for genomic sequences at single nucleotide resolution, HyenaDNA, based on the Hyena operation, has been constructed, capable of processing up to 1 million bases of context at once, breaking through the length and resolution limitations of traditional Transformers in DNA sequence modeling.
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach
Nurendra Choudhary (Virginia Tech), Chandan K. Reddy (Virginia Tech)
CodeMeta LearningGraph Neural NetworkGraph
π― What it does: This paper proposes a scalable hyperbolic space graph neural network meta-learning framework H-GRAM, which learns transferable prior biases from local subgraphs and enables rapid few-shot learning on new subgraphs.
π― What it does: Designed and implemented a Gaussian distribution statistical manifold-based variational autoencoder (GM-VAE), and conducted experiments on two major tasks: image density estimation and model-based reinforcement learning.
Hypervolume Maximization: A Geometric View of Pareto Set Learning
Xiaoyuan Zhang (City University of Hong Kong), Qingfu Zhang (Hong Kong Baptist University)
CodeOptimization
π― What it does: By constructing a neural network model with polar angles as input and transforming Pareto set learning into a geometric problem of maximizing hypervolume, the approach achieves approximation and prediction of the entire Pareto set.
π― What it does: A hypergraph-based table language model called HYTREL is proposed, which utilizes the hypergraph structure to unify the representation of table cell units, rows, columns, and the entire table, and conducts table representation learning based on this.
π― What it does: A two-stage irreversible backdoor attack (IBA) is proposed under the federated learning framework, which first learns a visually covert trigger generator and then gradually injects the backdoor into the global model without affecting normal classification performance.
π― What it does: This paper proposes an Intervention Driven Relationship Network (IDRNet) based on Deletion Diagnostics, which first aggregates pixel-level features into semantic-level representations, and then updates the semantic-level relationship matrix using deletion diagnostics to enhance the mutual reinforcement of semantic-level representations. Finally, the enhanced representations are fed back to the pixel features to improve the pixel-level prediction accuracy of semantic segmentation.
π― What it does: An Iterative Elastic Bin (IEBins) framework is proposed, which implements monocular depth estimation through multi-stage small bin iterative refinement search combined with a Swin Transformer encoder and a GRU iterative optimizer.
Michael Tschannen (Google DeepMind), Lucas Beyer (Google DeepMind)
CodeGenerationRetrievalTransformerVision Language ModelContrastive LearningImageText
π― What it does: Evaluate the performance and capabilities of Contrastive Pre-training on Image-Text Pairs (CLIP) versus pure image captioning pre-training on visual encoders.
π― What it does: Constructed the ImageReward human preference scoring model and implemented the ReFL direct tuning strategy for diffusion models based on it.
π― What it does: A text-to-image (T2I) system based on scene graph (SG) hallucination is proposed, capable of generating complex and detailed images from brief abstract texts.
Xin-Qiang Cai (University of Tokyo), Masashi Sugiyama (RIKEN AIP)
CodeRobotic IntelligenceReinforcement Learning from Human FeedbackReinforcement LearningGenerative Adversarial NetworkSequential
π― What it does: Proposes the Vaguely Pairwise Imitation Learning (VPIL) framework, and presents the algorithm COMPILER/COMPILER-E, which reconstructs the expert occupancy distribution through risk rewriting and mixed ratio estimation under known or unknown expert ratio Ξ±, combined with adversarial IL methods like GAIL.
Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
Zhu Wang (University of Illinois at Chicago), Sathya N. Ravi (University of Illinois at Chicago)
CodeAnomaly DetectionOptimizationComputational EfficiencyTransformerVision Language ModelMultimodality
π― What it does: An implicit differentiable outlier detection layer is proposed, which combines external knowledge graphs to efficiently eliminate noisy concepts in multimodal models, enhancing training efficiency and robustness.
π― What it does: This paper proposes a technique (IMGP) that can adaptively infer implicit low-dimensional manifolds from both labeled and unlabeled data, and construct Gaussian process regression models on that manifold.
π― What it does: Proposes the Implicit Transfer Operator (ITO) framework, which uses conditional denoising diffusion models to approximate molecular dynamics across multiple time scales;
π― What it does: This study investigates the mechanism by which the predictor and stop-gradient prevent representation collapse through implicit variance regularization in non-contrastive self-supervised learning. It derives the feature space dynamics of Euclidean and cosine losses and proposes IsoLoss for isometric convergence based on this.
π― What it does: A linearized implicit variational inference (LIVI) method is proposed, which uses a neural sampler to approximate high-dimensional posteriors, avoiding the training of adversarial objectives;
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
Hanlin Zhu (University of California Berkeley), Jiantao Jiao (University of California Berkeley)
CodeReinforcement LearningTabular
π― What it does: This paper proposes A-Crab, a new offline reinforcement learning algorithm that utilizes an actor-critic structure combined with importance-weighted average Bellman error to achieve a relatively pessimistic evaluation of the policy.
π― What it does: This paper discusses that improving data representation quality (Bayes risk) in a multi-model competitive environment does not necessarily enhance overall user prediction accuracy (social welfare) and may even lead to non-monotonic social losses.
π― What it does: This paper addresses the uncertainty quantification in graph node classification and proposes and validates an improved method based on distance regularization, aiming to enhance the performance of OOD detection and misclassification detection.
Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
Oussama Boussif (Mila - Quebec AI Institute), Yoshua Bengio (Mila - Quebec AI Institute)
CodeTransformerOptical FlowMultimodalityTime Series
π― What it does: A deep learning model based on satellite spatiotemporal context, CrossViViT, is proposed for predicting solar irradiance (GHI) time series one day in advance, achieving multi-quantile predictions to output confidence intervals.
π― What it does: Improve the adversarial robustness of deep networks through Information Bottleneck Distillation (IBD) combined with adversarial training.
π― What it does: This paper proposes a single-stage intermediate layer attack method called ILPD, which significantly enhances the transferability of attacks on unknown target models by attenuating perturbations in the feature space, aligning the perturbation direction with the guidance, and increasing the perturbation magnitude.
Lijie Fan (Google Research), Yonglong Tian (Google Research)
CodeRetrievalRepresentation LearningTransformerLarge Language ModelContrastive LearningImageTextMultimodality
π― What it does: The contextual learning ability of large language models is used to generate multiple rewritten versions of each image description in CLIP training, forming text data augmentation. During training, original or rewritten text is randomly selected to pair with images for contrastive learning.
π― What it does: A self-supervised learning framework based on Persistent Homology is proposed, which improves the molecular embedding space by providing multi-scale views and distance constraints through Persistence Images, and presents two methods: Topological Fingerprints AutoEncoder (TAE) and Topological Distance Contrastive Loss (TDL).
π― What it does: A method for multimodal emotion recognition with missing integrity, named IMDer, is proposed. It utilizes a score-based diffusion model to recover the missing modality under the condition of observed modalities, and then inputs the complete modalities into a multimodal transformer for emotion prediction.
Carol Xuan Long (Harvard University), Flavio Calmon
CodeTabular
π― What it does: This paper studies the significant increase in individual-level randomness (predictive multiplicity) of model predictions after implementing group fairness interventions in machine learning, and proposes a voting ensemble-based algorithm to reduce this multiplicity, ensuring more stable model predictions while maintaining fairness and accuracy.
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
Kenneth Li (Harvard University), Martin Wattenberg (Harvard University)
CodeGenerationOptimizationTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningText
π― What it does: This paper proposes an Inference-Time Intervention (ITI) technique that enhances the sincerity of large language models by fine-tuning the activation vectors of attention heads during the inference process.
Information Design in Multi-Agent Reinforcement Learning
Yue Lin (Chinese University of Hong Kong), Baoxiang Wang (Chinese University of Hong Kong)
CodeRecommendation SystemReinforcement Learning
π― What it does: A Markov Signaling Game framework is proposed to study information design in Multi-Agent Reinforcement Learning (MARL); it designs a signaling gradient that considers the receiver's strategy chain and learnable extended obedience constraints, enabling the sender to influence the receiver's adaptive learning through information in a mixed-motivation environment.
CodeClassificationExplainability and InterpretabilityComputational EfficiencyContrastive LearningImage
π― What it does: This paper provides an in-depth comparison between Information Pursuit (IP) and Orthogonal Matching Pursuit (OMP), demonstrating that OMP is a special case of IP, and based on this, proposes the IP-OMP algorithm; subsequently, it applies IP-OMP to interpretable predictions in visual classification by combining CLIP text/image embeddings, generating sparse explanations based on semantic concepts.
Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills
Denis Blessing (Karlsruhe Institute of Technology), Gerhard Neumann (Karlsruhe Institute of Technology)
CodeRobotic IntelligenceReinforcement Learning from Human FeedbackReinforcement LearningMixture of ExpertsSequential
π― What it does: An Information Maximization Curriculum (IMC) framework is proposed, which dynamically focuses on representable data subsets using curriculum weights and extends it to a mixture of experts model to address performance degradation caused by modality averaging.
Information-guided Planning: An Online Approach for Partially Observable Problems
Matheus Aparecido Do Carmo Alves, Leandro Soriano Marcolino (Lancaster University)
CodeOptimizationReinforcement Learning
π― What it does: An information entropy-guided POMCP algorithm (IB-POMCP) is proposed, which adaptively adjusts the exploration coefficient through entropy estimation in partially observable environments and improves action selection using the I-UCB function to achieve online planning for sparse reward problems.
Inserting Anybody in Diffusion Models via Celeb Basis
Ge Yuan (Sun Yat-sen University), Huicheng Zheng (Sun Yat-sen University)
CodeGenerationData SynthesisDiffusion modelImage
π― What it does: Quickly inject a new identity into a pre-trained Stable Diffusion using a single face photo, enabling arbitrary poses and interactive generation with other concepts.
π― What it does: A pseudo-label selection method based on instance-dependent thresholds, called InstanT, is proposed, along with theoretical guarantees.
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Wenliang Dai (Salesforce Research), Steven Hoi (Salesforce Research)
CodeClassificationRecognitionGenerationTransformerLarge Language ModelPrompt EngineeringVision Language ModelImageTextMultimodality
π― What it does: A framework for instruction tuning based on BLIP-2, called InstructBLIP, has been constructed to complete general visual language tasks using a multimodal language model.
Instructing Goal-Conditioned Reinforcement Learning Agents with Temporal Logic Objectives
Wenjie Qiu (Rutgers University), He Zhu (Rutgers University)
CodeRobotic IntelligenceReinforcement LearningAgentic AI
π― What it does: This study investigates how to enable goal-conditioned reinforcement learning agents to complete tasks with zero samples upon receiving linear temporal logic (LTL) tasks.
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
Zhengxuan Wu (Stanford University), Noah Goodman
CodeExplainability and InterpretabilityLarge Language ModelTextFinance Related
π― What it does: This paper uses the Boundless DAS method to provide causal explanations for the Alpaca (7B) model, discovering that it internally uses two Boolean variables for left and right boundary checks when performing price tagging tasks.
Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction
Quentin Delfosse (Technical University of Darmstadt), Kristian Kersting (Technical University of Darmstadt)
CodeOptimizationExplainability and InterpretabilityReinforcement LearningImage
π― What it does: This study proposes a reinforcement learning framework named NUDGE, which utilizes neural network-guided symbolic abstraction to generate interpretable logical policies, and achieves policy optimization through differentiable reasoning and PPO training.
Francesco Giannini (CINI), Pietro Barbiero (UniversitΓ della Svizzera Italiana)
CodeClassificationExplainability and InterpretabilityGraph Neural NetworkReinforcement LearningGraph
π― What it does: This study proposes a complete AI framework that first automatically generates a finite lattice dataset suitable for training through algorithms, and constructs graphical representations for propositional properties in Universal Algebra (UA); subsequently, an interpretable Graph Neural Network (iGNN) layer is designed, which compresses node features into interpretable concepts using Gumbel-Softmax and makes decisions directly with a linear classifier; finally, classification experiments are conducted on the lattice dataset using this model to test and validate existing UA conjectures, while also discovering new subgraph patterns.
Interpretable Prototype-based Graph Information Bottleneck
Sangwoo Seo (Korea Advanced Institute of Science and Technology), Chanyoung Park (Korea Advanced Institute of Science and Technology)
CodeClassificationExplainability and InterpretabilityGraph Neural NetworkContrastive LearningGraph
π― What it does: A novel explainable graph neural network framework called PGIB is developed, which combines prototype learning with the information bottleneck to automatically extract key subgraphs and provide visual explanations for graph classification tasks.
Interpreting Unsupervised Anomaly Detection in Security via Rule Extraction
Ruoyu Li (Tsinghua University), Yong Yang (Tsinghua University)
CodeAnomaly DetectionExplainability and InterpretabilityAuto EncoderTabular
π― What it does: This paper proposes a method for globally interpretable unsupervised anomaly detection models, utilizing rule extraction to construct interpretable and deployable rule sets.
Intervention Generalization: A View from Factor Graph Models
Gecia Bravo-Hermsdorff (University College London), Ricardo Silva (University College London)
CodeGraph Neural NetworkGraph
π― What it does: This paper proposes an Interventional Factor Model (IFM) based on factor graphs, providing identifiable conditions through a minimally assumed factorization structure, and implementing inference of expected outcomes under unobserved interventions using message passing and algebraic methods.
π― What it does: Under distribution shift, a Partial Condition Invariant Regularization (PCIR) is proposed and implemented from a causal perspective to enhance the robustness of anomaly detection models against domain and covariate shifts.
Invariant Learning via Probability of Sufficient and Necessary Causes
Mengyue Yang (University College London), Jun Wang (University College London)
CodeDomain AdaptationRepresentation LearningImage
π― What it does: This paper proposes a risk function based on the Probabilistic Necessary and Sufficient (PNS) causality to learn representations that incorporate both necessary and sufficient causal information during source domain training, thereby achieving robust generalization to unseen domains.
π― What it does: This paper studies the use of Inverse Dynamics Pretraining in multi-task imitation learning to learn low-dimensional representations of visual inputs and fine-tune them for new tasks.
π― What it does: This paper proposes Inverse Preference Learning (IPL), a method that directly uses the Q-function to learn implicit rewards in offline preference reinforcement learning, eliminating the need for an explicit reward network.
π― What it does: The study investigates whether the layers of ReLU networks maintain equivariance after training for group equivariance, and proposes the GCNN barrier metric to measure the network's proximity to GCNN.
π― What it does: A label-preserving data augmentation method named IPMix is proposed, which integrates image-level, patch-level, and pixel-level augmentations, and incorporates synthetic images such as fractals to enhance structural diversity, thereby training a more robust classifier.
Is Distance Matrix Enough for Geometric Deep Learning?
Zian Li (Peking University), Muhan Zhang (Peking University)
CodeGraph Neural NetworkGraph
π― What it does: This paper studies the use of distance matrices for deep learning on geometric graphs and proposes the k-DisGNN model based on k-WL to address the expressiveness incompleteness of the traditional Vanilla DisGNN, further demonstrating its learnability of high-order geometric information and unifying DimeNet and GemNet.
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
Jiawei Liu, LINGMING ZHANG
CodeAI Code AssistantTransformerLarge Language ModelText
π― What it does: The EvalPlus framework is proposed, which automatically generates a large number of test cases through LLM and mutation techniques, expanding and evaluating the functional correctness of LLM code generation; based on this, HUMANEVAL is upgraded to generate HUMANEVAL+ and its compressed version HUMANEVAL+-MINI.
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
Tianyu Chen (University of Texas at Austin), Pradeep Kumar Ravikumar
CodeScore-based ModelGraphTabularBiomedical Data
π― What it does: This study proposes an unsupervised method called iSCAN, which utilizes the Jacobian of the score matrix of mixed distributions to identify changes in causal mechanisms between different environments in nonlinear additive noise models (ANM). It can locate varying variables and further estimate structural changes.
π― What it does: Designed and implemented a Jaccard Metric Loss (JML) compatible with soft labels, combining it with label smoothing, knowledge distillation, and semi-supervised learning to enhance the accuracy and calibration performance of semantic segmentation models.
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Tristan Deleu (Mila Quebec AI Institute), Yoshua Bengio (Mila Quebec AI Institute)
CodeGraph Neural NetworkFlow-based ModelBiomedical Data
π― What it does: A single Generative Flow Network (JSP-GFN) is constructed to approximate the joint posterior distribution of the structure and conditional probability distribution parameters of Bayesian networks.
π― What it does: A graph OOV generalization method utilizing Label and Environmental Causal Independence (LECI) is proposed, aiming to discover causal subgraphs for robust prediction.
Joint processing of linguistic properties in brains and language models
SUBBA REDDY OOTA, Mariya Toneva (Microsoft)
CodeTransformerLarge Language ModelTextMultimodalityMagnetic Resonance Imaging
π― What it does: This study investigates the impact of different syntactic/semantic attributes in language models (BERT, GPT-2) on the alignment with human brain fMRI recordings, using a linear removal method to directly assess the contribution of each attribute to brain signal prediction performance.
Joint Prompt Optimization of Stacked LLMs using Variational Inference
Alessandro Sordoni (Microsoft Research), Nicolas Le Roux (Microsoft Research)
CodeTransformerLarge Language ModelPrompt EngineeringTextChain-of-Thought
π― What it does: Design and train a Deep Language Network (DLN) by stacking LLM layers and jointly optimizing prompts to improve the performance of small LLMs on reasoning and understanding tasks.
Joint Training of Deep Ensembles Fails Due to Learner Collusion
Alan Jeffares (University of Cambridge), Mihaela van der Schaar (University of Cambridge)
CodeConvolutional Neural NetworkImageTabular
π― What it does: This paper studies why the joint training of deep ensemble models performs poorly and reveals the phenomenon of 'learner collusion'.
K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing
Shuai Li (East China Normal University), Yanfeng Yang (East China Normal University)
CodeClassificationComputational EfficiencyTabular
π― What it does: A conditional independence testing method based on k-nearest neighbor local sampling and classifier estimation is proposed, which can control the first type error and maintain high test power in high-dimensional conditional variable and small sample situations.
π― What it does: This paper proposes a continuous kernel quadrature method based on Random Pivot Cholesky (RP-Cholesky) for generating efficient quadrature nodes and weights in arbitrary spaces, measures, and kernels.
Kernel-Based Tests for Likelihood-Free Hypothesis Testing
Patrik Robert Gerber, Rui Sun (Massachusetts Institute of Technology)
CodeImagePhysics Related
π― What it does: A mixed likelihood-free hypothesis testing (mLFHT) model is proposed, and a learnable kernel test statistic is constructed based on MMD;
π― What it does: Under low-label conditions, a Keypoint Augmentation Fusion layer (KAF) was designed to inject long-range self-attention into the UNet backbone, and a model was pre-trained through global and local self-supervised learning (SSL), followed by fine-tuning on a small amount of labeled data to achieve medical image segmentation.
π― What it does: This paper proposes DiffKD, a method that utilizes diffusion models to denoise student model features before knowledge distillation; by treating student features as a noisy version of teacher features, a trained diffusion model is used to progressively denoise them, resulting in cleaner features that better align with the teacher distribution, which enhances the distillation effect.
π― What it does: A knowledge-enhanced reasoning distillation method KARD is proposed, which combines reasoning generated by large models with external knowledge retrieval to fine-tune small language models, enabling them to generate reasonable justifications and provide correct answers in knowledge-intensive reasoning tasks.
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Yong Liu (Tsinghua University), Mingsheng Long (Tsinghua University)
CodeOptimizationAuto EncoderTime Series
π― What it does: The Koopa model is designed and implemented, which decomposes non-stationary time series into time-invariant and time-variant components using the Koopman theory, employing stacked Koopman Predictor blocks for hierarchical forecasting.
Petar Bevanda (TU Munich), Sandra Hirche (TU Munich)
CodeTime Series
π― What it does: This paper proposes and implements Koopman Kernel Regression (KKR), which utilizes RKHS to learn linear time-invariant (LTI) predictors, achieving multi-step predictions from system trajectories to future states.
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
Julia Linhart (Universite Paris-Saclay), Pedro L. C. Rodrigues (Univ. Grenoble Alpes)
CodeFlow-based ModelTabularBenchmark
π― What it does: A local diagnostic method β-C2ST is proposed to assess the local consistency of posterior approximations in Simulation-Based Inference (SBI). This method only requires samples from the joint distribution, can be tested under any given observation, and provides interpretable graphical diagnostics.
π― What it does: A unified language-driven image coloring model L-CAD is proposed, which can accept text descriptions at any level (complete, partial, sparse) and generate colored images that match the descriptions.
Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model
Hui Guo (University of Western Ontario), Grace Yi
CodeClassificationImage
π― What it does: A Bayesian framework-based instance-dependent noise transfer matrix model is proposed, which is used to correct crowd-sourced noisy labels, ultimately training a more accurate classifier.
π― What it does: This paper proposes a Label-Only model inversion attack method based on knowledge transfer, called LOKT. It first trains a generative adversarial network T-ACGAN using the hard labels from the target model to obtain a proxy model, and then performs a white-box attack on the proxy model to reconstruct private data.
Labeling Neural Representations with Inverse Recognition
Kirill Bykov (ATB Potsdam), Marina MC HΓΆhne
CodeExplainability and InterpretabilityComputational EfficiencyConvolutional Neural NetworkTransformerImage
π― What it does: The INVERT method is proposed, which uses inverse recognition technology to associate the internal representations of neural networks with interpretable concepts, thereby providing global explanations for individual neurons or sub-networks.
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
Viraj Uday Prabhu (Georgia Institute of Technology), Judy Hoffman (Georgia Institute of Technology)
CodeGenerationAdversarial AttackTransformerLarge Language ModelPrompt EngineeringDiffusion modelImageText
π― What it does: An automated method called LANCE is proposed, which uses language-guided adversarial image generation to stress test trained visual models and generate diverse, realistic, and challenging adversarial samples.
Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
Xiaoming Shi (Ant Group), Hongyuan Mei (TTIC)
CodeTransformerLarge Language ModelPrompt EngineeringTextTime SeriesSequentialRetrieval-Augmented Generation
π― What it does: The LAMP framework is proposed, utilizing large language models for spontaneous reasoning to provide causal evidence for event sequence models, thereby enhancing the predictive performance of future events.
Geunwoo Kim (University of California), Stephen Marcus McAleer
CodeTransformerLarge Language ModelReinforcement LearningPrompt EngineeringTextChain-of-Thought
π― What it does: A recursive critique and improvement (RCI) prompting method based on large language models is proposed to perform computer tasks and enhance reasoning capabilities.
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
Miles Turpin (New York University), Samuel R. Bowman (New York University)
CodeExplainability and InterpretabilityAdversarial AttackTransformerLarge Language ModelPrompt EngineeringTextBenchmarkChain-of-Thought
π― What it does: This paper explores the trustworthiness of Chain of Thought (CoT) explanations through experiments with large language models (GPT-3.5 and Claude-1.0) under CoT prompting. It finds that the models are susceptible to input biases (such as always suggesting answer A) which lead to unfaithful CoT explanations, resulting in decreased accuracy.
Language Models Meet World Models: Embodied Experiences Enhance Language Models
Jiannan Xiang (University of California San Diego), Zhiting Hu (University of California San Diego)
CodeTransformerLarge Language ModelSupervised Fine-TuningWorld ModelText
π― What it does: Fine-tune pre-trained language models using embodied experiences collected from a virtual home environment (VirtualHome) to enhance their reasoning and planning capabilities in physical environments.
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment
Hao Liu (University of California Berkeley), Pieter Abbeel (University of California Berkeley)
CodeGenerationRepresentation LearningTransformerLarge Language ModelAuto EncoderImageText
π― What it does: Proposes the Language Quantized AutoEncoder (LQAE), a variant of VQ-VAE that unsupervisedly maps images to text space, utilizing a pre-trained language denoising model to generate text encodings and perform image reconstruction;
Language Semantic Graph Guided Data-Efficient Learning
Wenxuan Ma (Beijing Institute of Technology), Jingxuan Kang (University of Liverpool)
CodeClassificationData-Centric LearningGraph Neural NetworkLarge Language ModelSupervised Fine-TuningImageVideoAudio
π― What it does: This paper proposes a framework that utilizes label semantic information through a Language Semantic Graph (LSG) to enhance data-efficient learning;