CLIP-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments
Jessica Dai (University of California Berkeley), Christopher Harshaw (Massachusetts Institute of Technology)
CodeOptimizationSequential
🎯 What it does: This study proposes an adaptive Neyman allocation experimental design called CLIP-OGD, aimed at improving the accuracy of causal inference in sequential experiments.
Closing the Computational-Statistical Gap in Best Arm Identification for Combinatorial Semi-bandits
Ruo-Chun Tzeng (KTH Royal Institute of Technology), Chi-Jen Lu (Academia Sinica)
CodeOptimization
🎯 What it does: The study investigates the problem of best arm identification in combinatorial semi-bands and proposes an algorithm called Perturbed Frank-Wolfe Sampling (P-FWS).
🎯 What it does: A clustering-based Transformer model called CLUSTERFORMER is proposed, which can uniformly handle image classification, detection, and segmentation tasks.
CoDet: Co-occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
Chuofan Ma (University of Hong Kong), XIAOJUAN QI
CodeObject DetectionVision Language ModelImageText
🎯 What it does: The CoDet framework is proposed, which utilizes image clustering of the same concept in image-text pairs to discover co-occurring objects through cross-image region similarity, thereby achieving automatic generation of unannotated region-word pairs and training an open vocabulary object detector.
CoDrug: Conformal Drug Property Prediction with Density Estimation under Covariate Shift
Siddhartha Laghuvarapu (University of Illinois), Jimeng Sun (University of Illinois)
CodeDrug DiscoveryGraph Neural NetworkTabularBiomedical Data
🎯 What it does: The CoDrug method is proposed to achieve adaptive confidence sets that conform to coverage in drug property prediction under covariate shift through energy models and kernel density estimation.
🎯 What it does: This paper proposes and implements a deep visual network based on Long-Range Modulation Feedback (LRM), utilizing multi-scale learnable multiplicative modulation to achieve cognitive steering, thereby enhancing classification performance, attack robustness, and brain alignment under default operation and target guidance.
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
Andres Potapczynski (New York University), Andrew Gordon Wilson (New York University)
CodeOptimizationComputational EfficiencyTabular
🎯 What it does: Designed and implemented the CoLA framework, which automates large-scale structured linear algebra operations through a combination of linear operator abstraction and multiple dispatch rules, supporting automatic differentiation, low precision, and GPU acceleration.
Fereshte Khani (Microsoft), Marco Tulio Ribeiro (Google DeepMind)
CodeClassificationTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: A collaborative model alignment framework called CoAlign is proposed, which improves the compliance of NLP models with business rules and values by allowing multiple users to jointly define concepts and using the controversial areas between local concept models and global models to label data.
Dongyang Fan (École Polytechnique Fédérale de Lausanne), Martin Jaggi (École Polytechnique Fédérale de Lausanne)
CodeFederated LearningKnowledge DistillationImage
🎯 What it does: A collaborative learning algorithm based on predictive consensus is proposed, utilizing shared unlabeled data and adaptive trust weights to achieve pseudo-label collaborative training among multiple models.
Collaboratively Learning Linear Models with Structured Missing Data
Chen Cheng (Stanford University), John Duchi (Stanford University)
CodeOptimizationFederated LearningTabular
🎯 What it does: This study designs a communication-efficient distributed algorithm named COLLAB, aimed at collaboratively learning linear models and improving prediction accuracy in multi-agent environments with structured missing features.
🎯 What it does: A Bayesian deep learning inference framework called CIBER is proposed, which is based on folded sampling and weighted model integration (WMI) to efficiently approximate Bayesian model averaging (BMA).
🎯 What it does: A color equivariant convolution (CEConv) is proposed, achieving equivariance to color shifts by sharing parameters in the hue space while preserving color information.
🎯 What it does: A robust graph link prediction framework for bidirectional edge noise (which simultaneously affects input topology and target labels) is proposed—Robust Graph Information Bottleneck (RGIB). Two implementable instances are provided: RGIB-SSL based on self-supervised learning and RGIB-REP based on data reparameterization, aimed at enhancing the link prediction performance of graph neural networks on noisy graphs.
🎯 What it does: This paper proposes a self-supervised learning method called Geometric Harmonization (GH) to alleviate representation learning discrepancies under long-tailed data distributions.
🎯 What it does: This paper proposes COMPASS, a method for finding optimal solutions in combinatorial optimization by conditioning strategies on the latent space and using evolutionary search during inference.
Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
Jiaxin Bai (Hong Kong University of Science and Technology), Yangqiu Song (Hong Kong University of Science and Technology)
CodeGraph
🎯 What it does: This paper proposes a Complex Eventuality Query Answering (CEQA) framework for executing complex logical queries with implicit logical constraints (occurrence and temporal order) on Event Knowledge Graphs (EVKG).
🎯 What it does: The paper redefines the latent space from the perspective of model complexity, introduces the concepts of GAN-induced distance and optimal latent distribution, and proposes a two-stage Decoupled Autoencoder training method.
Composing Parameter-Efficient Modules with Arithmetic Operation
Jinghan Zhang (Hong Kong University of Science and Technology), Junxian He (Hong Kong University of Science and Technology)
CodeDomain AdaptationOptimizationTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: The study efficiently performs addition and negation operations on trained parameter-efficient modules (LoRA, IA3) in the parameter space, allowing for the synthesis of new modules without additional training, supporting various scenarios such as distribution generalization, multi-tasking, de-learning, domain transfer, and de-biasing.
🎯 What it does: This paper redefines compositionality from the ground up, proposing a compositional data generation model that satisfies identifiability conditions during the generation process, and proves that under certain support conditions, the model can achieve consistent generalization on compositional samples outside the training distribution.
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
Đorđe Žikelić (Institute of Science and Technology Austria), Thomas A Henzinger
CodeOptimizationReinforcement LearningSequential
🎯 What it does: A composable reinforcement learning framework CLAPS is proposed for learning neural network policies in stochastic control systems, providing probabilistic guarantees that satisfy the SPECTRL specification.
🎯 What it does: A general 'Compositional Sculpting' framework is proposed, which enables the combination and reshaping of the base distribution through observed variables in iterative generative models (such as GFlowNets and diffusion models), supporting binary operations like harmonic mean and contrast, and allowing direct sampling guided by classifiers.
Compression with Bayesian Implicit Neural Representations
Zongyu Guo (University of Science and Technology of China), José Miguel Hernández-Lobato (University of Cambridge)
CodeCompressionImageAudio
🎯 What it does: A compression framework based on Variational Bayesian Implicit Neural Representation (INR) is proposed—COMBINER, which compresses the posterior samples of INR weights using relative entropy coding to directly achieve rate-distortion optimization;
Computing Optimal Nash Equilibria in Multiplayer Games
Youzhi Zhang (Hong Kong Institute of Science and Innovation), Venkatramanan Siva Subrahmanian
CodeOptimization
🎯 What it does: This paper proposes a new algorithm called CRM based on correlation plans and their relational constraints, aimed at solving the optimal Nash Equilibrium (NE) in multi-player games, specifically optimizing a given objective function over the entire NE space.
🎯 What it does: A Composite Speech-Language (ComSL) end-to-end speech-to-text translation model is constructed, combining the Whisper speech encoder, mBART language decoder, and adapters, utilizing multi-task learning and cross-modal learning (CML) for efficient model fine-tuning.
🎯 What it does: Introducing Conditional Adapter (CODA) based on pre-trained Transformers, which adds small adapters and learnable routers at each layer to select a subset of tokens for high-cost Transformer computations, performing deep inference only on important tokens to enhance inference efficiency.
Conditional independence testing under misspecified inductive biases
Felipe Maia Polo (University of Michigan), Moulinath Banerjee (University of Michigan)
CodeTabular
🎯 What it does: This paper studies the performance of regression-based conditional independence (CI) tests under model misspecification and proposes a new robust testing method called the Rao-Blackwellized Predictor Test (RBPT).
Conditional Matrix Flows for Gaussian Graphical Models
Marcello Massimo Negri (University of Basel), Volker Roth (University of Basel)
CodeFlow-based ModelGraphTabularBiomedical Data
🎯 What it does: A Conditional Matrix Flow (CMF) model is constructed, achieving variational inference of the precision matrix in high-dimensional Gaussian Graphical Models (GGM) by constructing conditional normalizing flows in the space of symmetric positive definite matrices. It can simultaneously obtain Bayesian posterior, frequentist solution paths, and marginal likelihood for model selection.
🎯 What it does: Using a pre-trained text-to-image diffusion model, we design a training-free text-driven image-to-image translation method that selectively edits the target image based on the source image and source text while keeping the background unchanged.
🎯 What it does: A conditional sampling method based on unconditional score diffusion models in infinite-dimensional Hilbert space is proposed, achieving full probabilistic inference for Bayesian inverse problems.
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
Ahmed Alaa, Mark van der Laan (University of California Berkeley)
CodeMeta LearningTabular
🎯 What it does: A conformal meta-learner framework based on two-stage pseudo-observation regression has been developed to provide prediction intervals for individual treatment effects (ITE).
Conformal Prediction for Time Series with Modern Hopfield Networks
Andreas Auer (Johannes Kepler University Linz), Sepp Hochreiter (Johannes Kepler University Linz)
CodeAnomaly DetectionOptimizationRecurrent Neural NetworkTime Series
🎯 What it does: A synthetic forecasting method for time series, HopCPT, is proposed, which generates narrower prediction intervals while maintaining the coverage of confidence intervals.
🎯 What it does: A consistency constraint (Consistency Property, CP) is proposed, which forces the model to maintain self-consistency in the generated trajectories during training, thereby reducing sampling drift in diffusion models.
🎯 What it does: A constraint policy optimization method for offline reinforcement learning, CPED, is proposed. It uses Flow-GAN for explicit density estimation of the behavior policy and incorporates a safety region constraint based on that density into policy learning, reducing excessive conservativeness towards OOD points.
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars
Simon Schrodi (University of Freiburg), Frank Hutter (University of Freiburg)
CodeNeural Architecture SearchTransformerImage
🎯 What it does: A unified framework based on Context-Free Grammar (CFG) is proposed to construct a scalable hierarchical neural architecture search space.
🎯 What it does: This paper proposes a meta-learning topic model called Meta-CETM, which is based on task-specific semantic graphs and a Gaussian mixture prior. It can quickly adapt to generate contextually relevant word embeddings and discover high-quality topics in low-resource scenarios.
🎯 What it does: This paper proposes the Contextually Affinitive Neighborhood Refinery (CoNR), which enhances deep clustering performance by mining richer neighbors through online reordering and introducing progressive boundary filtering.
Continual Learning for Instruction Following from Realtime Feedback
Alane Suhr (University of California), Yoav Artzi (Cornell University)
CodeRobotic IntelligenceReinforcement Learning from Human FeedbackReinforcement LearningSequential
🎯 What it does: A continuous learning framework based on real-time binary feedback has been designed and implemented, allowing the agent to improve continuously through interactions with human users.
🎯 What it does: A continuous autoregressive model, ContinuAR, has been designed and implemented to handle the infinite-order multi-fidelity fusion problem.
🎯 What it does: This paper proposes a hierarchical contrastive learning framework named COMET, designed to extract general representations of medical time series without relying on a large amount of labeled data.
🎯 What it does: High-resolution visual image decoding of brain activity is achieved through dual-stage fMRI representation learning and latent diffusion models.
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning
Siming Lan (University of Science and Technology of China), Yunji Chen (Intelligent Software Research Center Institute of Software CAS)
CodeRobotic IntelligenceMeta LearningReinforcement LearningMixture of ExpertsContrastive LearningSequential
🎯 What it does: This paper proposes the CMTA method, which achieves module differentiation in multi-task reinforcement learning through the use of contrastive learning and dynamically combines modules at each time step using temporal attention, significantly improving multi-task learning performance.
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL
Chen Sun (Mila), Blake Aaron Richards
CodeReinforcement LearningContrastive Learning
🎯 What it does: This paper studies a module called ConSpec that can be directly integrated into existing reinforcement learning algorithms. It utilizes offline contrastive learning to learn 'prototypes' from successful and failed experiences, in order to identify key steps in tasks and generate intrinsic rewards, thereby accelerating long-term credit assignment.
🎯 What it does: Improved the complex-valued autoencoder and enhanced object separation capability through contrastive learning, achieving unsupervised object discovery.
🎯 What it does: A nested SGDA algorithm based on policy gradient is proposed, which can achieve global optimality in zero-sum Markov Stackelberg games with continuous states and continuous actions by utilizing the noisy gradient of observed trajectories, and it is proven to converge in polynomial time; the reach-avoid problem is modeled as a convex-concave zero-sum Markov Stackelberg game, and experiments verify that the Stackelberg limit strategy outperforms the Nash strategy.
Convolution Monge Mapping Normalization for learning on sleep data
Theo Gnassounou (University Paris-Saclay), Alexandre Gramfort (University Paris-Saclay)
CodeDomain AdaptationConvolutional Neural NetworkTime SeriesBiomedical Data
🎯 What it does: A convolutional Monge mapping normalization method based on Wasserstein barycenter (CMMN) is proposed for unsupervised domain adaptation of sleep EEG data.
Convolutional Neural Operators for robust and accurate learning of PDEs
Bogdan Raonic (ETH Zurich), Emmanuel de Bezenac (ETH Zurich)
CodeConvolutional Neural NetworkBenchmarkPhysics Related
🎯 What it does: A convolutional neural operator learning framework is proposed—Convolutional Neural Operator (CNO), which achieves continuous-discrete equivalence by modifying basic operations such as convolution, upsampling/downsamping, and activation;
🎯 What it does: A new convolutional state space model (ConvSSM) is proposed for long-term spatiotemporal modeling, combining the advantages of convolutional LSTM and state space methods.
🎯 What it does: This paper proposes a single-stage open vocabulary segmentation framework called FC-CLIP, which combines a frozen convolutional CLIP backbone with Mask2Former to achieve mask generation and classification.
Coordinating Distributed Example Orders for Provably Accelerated Training
A. Feder Cooper (Cornell University), Christopher De Sa (Cornell University)
CodeOptimizationComputational EfficiencyRecurrent Neural NetworkSupervised Fine-TuningTabularTime Series
🎯 What it does: The CD-GraB algorithm is proposed, which accelerates training by coordinating the gradient order of each worker node in distributed training, utilizing provable permutation-based example sorting.
🎯 What it does: This paper proposes a composable core-set construction algorithm for approximate solutions to diversity maximization (FDM) tasks under fairness/partition constraints, focusing on three commonly used diversity metrics (minimum pairwise distance, total pairwise distance, and total nearest neighbor distance).
🎯 What it does: A convex optimization framework named CORNN is proposed for the rapid training of data-constrained recurrent neural networks (dRNN), enabling real-time network reconstruction of large-scale neural recordings.
🎯 What it does: A supervised deep neural network framework based on CorInfoMax (Correlation Information Maximization) is proposed, which can solve the weight symmetry problem and achieve a multi-chamber neuron model.
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
Jianzhun Shao (Tsinghua University), Xiangyang Ji (Tsinghua University)
CodeReinforcement LearningSequential
🎯 What it does: This paper proposes a Counterfactual Conservative Q Learning (CFCQL) algorithm for offline multi-agent reinforcement learning to address the overestimation problem caused by joint action distribution shift and to achieve offline training for team collaboration.
Counterfactual Evaluation of Peer-Review Assignment Policies
Martin Saveski (University of Washington), Johan Ugander (Stanford University)
Code
🎯 What it does: This paper constructs an offline policy evaluation framework using a random assignment strategy introduced in peer review to assess the impact of different allocation policies on review quality.
Yo Joong Choe (University of Chicago), Aaditya Ramdas (Carnegie Mellon University)
CodeClassificationImage
🎯 What it does: This study investigates how to evaluate and compare black-box non-rejecting (abandonable) classifiers and proposes a 'counterfactual score' metric;
🎯 What it does: This paper proposes a method to generate adversarial samples using all features and constructs representations that satisfy counterfactual fairness through symmetric functions, thereby training a predictive model that meets perfect counterfactual fairness.
Critical Initialization of Wide and Deep Neural Networks using Partial Jacobians: General Theory and Applications
Darshil Doshi (University of Maryland), Andrey Gromov (Meta)
CodeImage
🎯 What it does: This paper analyzes the properties of gradient propagation in deep neural networks at initialization by introducing the Partial Jacobian and its average norm (APJN), and proposes a low-cost, directly applicable criticality diagnostic method.
🎯 What it does: A dual-structure framework is proposed, utilizing supervised enhancement, dual GNNs, and embedding fusion to eliminate the prediction bias between cross-community links and internal links.
🎯 What it does: This paper addresses the issue of noisy correspondence in image-text matching and proposes a general robust framework called CRCL to enhance the performance of matching models in noisy environments.
🎯 What it does: By injecting a bidirectional spatial-channel-temporal attention module DG-SCT into a frozen large-scale pre-trained audio-visual encoder, the use of audio and visual as soft prompts dynamically adjusts feature extraction, significantly improving the performance of multimodal tasks.
CodeGenerationSafty and PrivacyDiffusion modelImage
🎯 What it does: A framework for unencapsulated image steganography based on diffusion models, CRoSS, is proposed, enabling information hiding and recovery without the need for training.
CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement
Qihe Huang (University of Science and Technology of China), Yang Wang (Suzhou Institute for Advanced Research)
CodeGraph Neural NetworkTime Series
🎯 What it does: This paper proposes CrossGNN, a graph neural network with linear complexity for multivariate time series (MTS) prediction, capable of simultaneously refining cross-scale and cross-variable interactions.
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion
Anders Vestergaard Nørskov (Technical University of Denmark), Morten Mørup (Technical University of Denmark)
CodeClassificationDomain AdaptationRepresentation LearningConvolutional Neural NetworkTransformerAuto EncoderContrastive LearningTime SeriesBiomedical Data
🎯 What it does: A contrastive learning + segmentation latent space autoencoder framework for zero-shot transformation of EEG signals (CSLP-AE) is proposed, achieving single-trial EEG transformation and feature extraction under unseen subjects and tasks.
🎯 What it does: This paper proposes a Curriculum Learning and Structure-Aware Optimal Transport (CSOT) method for denoising and re-labeling training samples in the presence of label noise, thereby enhancing the model's robustness and generalization performance.
Curvature Filtrations for Graph Generative Model Evaluation
Joshua Southern (Imperial College London), Bastian Rieck (Helmholtz Munich)
CodeGenerationData SynthesisOptimizationGraph Neural NetworkGraphBiomedical Data
🎯 What it does: A curvature filtering method that combines discrete curvature with topological data analysis is proposed to evaluate the distribution distance of graph generative models.
🎯 What it does: CycleNet is proposed, a framework for unpaired image-to-image translation that incorporates cyclic consistency regularization on a pre-trained text-guided latent diffusion model, achieving image + text dual-condition translation through a single ControlNet side network; it also contributes the multi-domain state change dataset ManiCups; additionally, a FastCycleNet variant for rapid training is provided.
🎯 What it does: A new method called D-CIPHER is proposed for discovering closed-form partial differential equations (PDEs) and their higher-order ordinary differential equations (ODEs) from noisy sparse data.
D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion
Jialin Chen (Yale University), Zhitao Ying
CodeExplainability and InterpretabilityAdversarial AttackGraph Neural NetworkDiffusion modelGraphTabular
🎯 What it does: A unified framework D4Explainer based on discrete denoising diffusion models is proposed for generating counterfactual and model-level explanations for graph neural networks.
Zhengdong Hu (University of Technology Sydney), Yi Yang (Baidu Inc.)
CodeObject DetectionTransformerImage
🎯 What it does: It was found that the effects of cross-attention and self-attention on queries in the DETR decoder are opposing, and an auxiliary decoder without self-attention (DAC-DETR) is proposed to separate and train these two types of attention independently to improve training efficiency and detection accuracy.
DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets
Yash Jain (Georgia Institute of Technology), Vibhav Vineet (Microsoft Research)
CodeObject DetectionDomain AdaptationTransformerMixture of ExpertsImageBenchmark
🎯 What it does: This paper proposes Dataset-aware Mixture-of-Experts (DAMEX), which replaces part of the FFN in the DINO detection framework with MoE and trains a router to focus each expert on the corresponding dataset, thereby achieving the fusion and general detection of multiple datasets within a single model.
🎯 What it does: A non-autoregressive two-step direct speech-to-speech translation model DASpeech is designed, which first generates the target text and then generates the target speech.
Data Selection for Language Models via Importance Resampling
Sang Michael Xie (Stanford University), Percy Liang (Stanford University)
CodeDomain AdaptationLarge Language ModelText
🎯 What it does: This paper proposes a data selection framework called DSIR based on importance resampling, aimed at selecting data that conforms to the target distribution from a vast amount of unlabeled text.
🎯 What it does: Utilize Stable Diffusion to generate synthetic images with pixel-level semantic segmentation labels, thereby constructing a dataset that can be directly used to train semantic segmentation models.
CodeSegmentationGenerationData SynthesisPose EstimationDepth EstimationTransformerLarge Language ModelPrompt EngineeringDiffusion modelImage
🎯 What it does: We propose DatasetDM, a text-to-data generation framework based on pre-trained diffusion models, capable of infinitely generating high-quality synthetic images along with corresponding perceptual annotations (semantic segmentation, instance segmentation, depth maps, human pose, etc.).
DAW: Exploring the Better Weighting Function for Semi-supervised Semantic Segmentation
Rui Sun (University of Science and Technology of China), Feng Wu (University of Science and Technology of China)
CodeSegmentationConvolutional Neural NetworkImage
🎯 What it does: A semi-supervised semantic segmentation method based on Distribution-Aware Weighting (DAW) is proposed, which systematically analyzes the trade-off between the use and discard of pseudo-labels, and derives the optimal weighting function by explicitly modeling the confidence distribution.
🎯 What it does: A two-stage probabilistic framework was developed to perform statistical downsampling using unpaired data. First, optimal transport is used to remove the bias of the low-resolution model, and then a conditional diffusion model is employed for upsampling to generate high-resolution flow field samples.
Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
Yutong Xia (National University of Singapore), Roger Zimmermann (National University of Singapore)
CodeGraph Neural NetworkGraphTime Series
🎯 What it does: The CaST framework is proposed to address the temporal out-of-distribution (OoD) and dynamic spatial causal issues in spatiotemporal graph prediction using causal inference methods.
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Sharan Vaswani (Simon Fraser University), Nicolas Le Roux (Microsoft Research)
CodeOptimizationReinforcement Learning
🎯 What it does: A decision-aware Actor-Critic (AC) framework is proposed, designing a joint objective and providing a general AC algorithm that can be used with any function approximation;
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses
Gon Buzaglo (Weizmann Institute of Science), michal Irani
CodeClassificationRestorationImage
🎯 What it does: This study investigates the reconstruction capability of neural networks for training samples and achieves sample reconstruction under multi-class classification, weight decay, and general loss functions.
Deductive Verification of Chain-of-Thought Reasoning
Zhan Ling (University of California San Diego), Hao Su (University of California San Diego)
CodeExplainability and InterpretabilityTransformerLarge Language ModelPrompt EngineeringTextChain-of-Thought
🎯 What it does: A 'Natural Program' format based on natural language is designed to enable large language models to generate decomposable and verifiable deductive reasoning chains, enhancing the credibility and interpretability of reasoning through step-by-step self-verification.
🎯 What it does: This paper proposes a fast implementation of the two-dimensional fractional Fourier transform (FRFT) and designs a multi-order fractional convolution operator (MFRFC) for processing images from spatial, frequency, and unified space-frequency perspectives.
🎯 What it does: A cautious pseudo-labeling (CPL) method is proposed, which conducts a theoretical analysis of the negative impact of pseudo-label noise on training in graph data and provides practical solutions.
🎯 What it does: An end-to-end deep learning method is proposed for reconstructing non-line-of-sight (NLOS) images from under-sampled measurements (USM), which includes an Instantaneous Recovery Network (TRN) and a Volume Reconstruction Network (VRN);
🎯 What it does: A framework is proposed that allows for optimal linear transport of outputs from existing recovery models during the testing phase with only a small number of samples, enhancing perceptual quality while preserving MSE.
Zachary Teed (Princeton University), Jia Deng (Princeton University)
CodePose EstimationOptimizationComputational EfficiencyRecurrent Neural NetworkSimultaneous Localization and MappingImage
🎯 What it does: This paper proposes a deep learning visual odometry system DPVO based on sparse image patches, achieving real-time monocular visual odometry using recurrent networks and differentiable bundle adjustment.
🎯 What it does: This paper proposes Markov Neural Processes (MNPs), which gradually transform a simple initial stochastic process into a more expressive generative stochastic process by stacking reversible Markov transition operators (i.e., reversible transformations) in function space, while maintaining exchangeability and consistency.
🎯 What it does: A deep reinforcement learning-based ant colony optimization framework, DeepACO, is designed to automate the heuristic design of the ant colony algorithm by learning to generate a global heuristic heatmap, and it is combined with ant colony search to improve the solution quality of various combinatorial optimization problems.
🎯 What it does: This paper proposes an end-to-end hand-object interaction 3D pose estimation framework called DeepSimHO, which can simultaneously obtain the poses of the hand and the object from a single image, ensuring grip stability through physical simulation.
Eden Saig (Technion - Israel Institute of Technology), Nir Rosenfeld (Technion - Israel Institute of Technology)
CodeClassificationOptimizationTabular
🎯 What it does: This study proposes a delegation classification framework for machine learning outsourcing, utilizing contract design to incentivize agents to provide high-quality models.
Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought
Huaxiaoyue Wang (Cornell University), Sanjiban Choudhury (Cornell University)
CodeRobotic IntelligenceAI Code AssistantTransformerLarge Language ModelPrompt EngineeringVideoTextChain-of-Thought
🎯 What it does: The Demo2Code framework is proposed, which recursively summarizes demonstration generation task specifications and expands them into executable robot code.
Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
Namkyeong Lee (Korea Advanced Institute of Science and Technology), Chanyoung Park (Korea Advanced Institute of Science and Technology)
CodeGraph Neural NetworkTransformerPrompt EngineeringMultimodalityPhysics Related
🎯 What it does: This study investigates how to predict the density of states (DOS) of crystal materials by combining crystal structure and energy information through a multimodal Transformer.
Derandomized novelty detection with FDR control via conformal e-values
Meshi Bashari (Technion Israel Institute of Technology), Matteo Sesia (University of Southern California)
CodeAnomaly DetectionTabular
🎯 What it does: This paper proposes a derandomized novelty detection method based on conformal e-values, utilizing multiple data splits and e-value aggregation to achieve FDR control in anomaly detection and reduce algorithm randomness.