Conference on Neural Information Processing Systems Β· 1874 papers
Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases
Hang Yin (Shanghai Jiao Tong University), Chenghu Zhou (Chinese Academy of Sciences)
CodeGraph Neural NetworkContrastive LearningGraph
π― What it does: A unified framework called Lambda is proposed to address the issue of unlabeled dangling entities in knowledge graph entity alignment. It first detects dangling entities through iterative PU learning, and then utilizes GNN to select and aggregate them, completing entity alignment with spectral contrastive learning.
Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
Eli Chien (Georgia Institute of Technology), Pan Li (Georgia Institute of Technology)
CodeOptimizationSafty and PrivacyConvolutional Neural NetworkImageStochastic Differential Equation
π― What it does: Proposes a Langevin Unlearning framework based on noise gradient descent, providing a 'forgotten' guarantee for approximate machine learning models.
Yuan He (University of Oxford), Ian Horrocks (University of Oxford)
CodeTransformerLarge Language ModelSupervised Fine-TuningTextBiomedical Data
π― What it does: Retrain a Transformer-based language model to become a Hierarchical Transformer Encoder (HIT) that explicitly encodes hierarchical structures.
CodeGenerationAutonomous DrivingTransformerLarge Language ModelPrompt EngineeringPoint Cloud
π― What it does: An interactive traffic trajectory generation model called InteractTraj has been developed, which can generate realistic trajectories containing interactions between vehicles based on user descriptions.
Large Language Model Unlearning via Embedding-Corrupted Prompts
Chris Yuhao Liu (University of California), Yang Liu (University of California)
CodeOptimizationKnowledge DistillationTransformerLarge Language ModelPrompt EngineeringText
π― What it does: A lightweight LLM unlearning framework called Embedding-Corrupted (ECO) Prompts is proposed, which learns perturbations of the embeddings of prompts that meet the forgetting conditions during the inference phase, allowing the model to 'forget' specified knowledge without updating weights.
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
Jiawei Wang (University of Tokyo), Chuan Xiao (Osaka University)
CodeGenerationRecommendation SystemExplainability and InterpretabilityTransformerLarge Language ModelAgentic AITime SeriesSequentialRetrieval-Augmented Generation
π― What it does: Using large language models (LLM) as agents, combined with real urban personal mobility trajectory data, a framework based on activity patterns and motivations is proposed to generate interpretable and semantically rich personal daily activity trajectories.
Large Language Models Must Be Taught to Know What They Donβt Know
Sanyam Kapoor (New York University), Andrew Gordon Wilson (New York University)
CodeTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringText
π― What it does: This paper studies the uncertainty estimation of large language models (LLMs) and proposes to achieve high-quality calibrated uncertainty by fine-tuning the model with LoRA+Prompt on a small amount of labeled data.
Large Language Models Play StarCraft II:Benchmarks and A Chain of Summarization Approach
Weiyu Ma (Institute of Automation, Chinese Academy of Sciences), Haifeng Zhang (Nanjing Artificial Intelligence Research of IA)
CodeTransformerLarge Language ModelReinforcement LearningPrompt EngineeringTextBenchmarkChain-of-Thought
π― What it does: Developed a textual StarCraft II environment (TextStarCraft II) and a summary-based decision-making process (Chain of Summarization, CoS), and conducted real-time strategy decision-making and evaluation using large language models (LLM) in this environment.
Large Pre-trained time series models for cross-domain Time series analysis tasks
Harshavardhan Kamarthi (Georgia Institute of Technology), B. Aditya Prakash (Georgia Institute of Technology)
CodeClassificationAnomaly DetectionOptimizationRecurrent Neural NetworkTransformerTime SeriesFinance Related
π― What it does: A cross-domain time series pre-training model LPTM is proposed, utilizing adaptive segmentation and self-supervised masking tasks, supporting zero-shot or few-shot fine-tuning to complete various prediction and classification tasks.
Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
Alessandro Montenegro (Politecnico di Milano), Alberto Maria Metelli (Politecnico di Milano)
CodeOptimizationReinforcement Learning
π― What it does: A general primal-dual algorithm based on policy gradient, C-PG, and its action-based and parameter-based versions, C-PGAE and C-PGPE, are proposed to solve constrained reinforcement learning problems in continuous control, supporting constraints based on risk metrics.
π― What it does: LDNS combines autoencoders with diffusion models to generate realistic, variable-length, and conditionally neural spike data in latent space, achieving low-dimensional latent variable inference.
Latent Neural Operator for Solving Forward and Inverse PDE Problems
Tian Wang (Chinese Academy of Sciences), Chuang Wang (Chinese Academy of Sciences)
CodeTransformerTime SeriesPhysics Related
π― What it does: Designed and implemented the Latent Neural Operator (LNO), which utilizes Physics-Cross-Attention (PhCA) to learn the operations of PDEs in a learnable latent space, capable of solving both forward and inverse problems.
π― What it does: This paper proposes a Latent Plan Transformer based on an implicit variational autoregressive model for planning tasks, utilizing trajectory-reward pairs as training data to directly infer trajectories from global rewards.
π― What it does: This paper studies a layer adaptive state pruning method called LAST for deep state space models, aimed at reducing the dimensionality of the model states and lowering computational and memory costs while maintaining performance.
π― What it does: A local constraint compact point cloud model named LCM is proposed for the Masked Point Modeling (MPM) task, which includes a locally aggregated encoder and a Mamba-based decoder.
Learn To be Efficient: Build Structured Sparsity in Large Language Models
Haizhong Zheng (University of Michigan), Atul Prakash (University of Michigan)
CodeOptimizationComputational EfficiencyTransformerLarge Language ModelMixture of ExpertsText
π― What it does: Train an algorithm called LTE to enable large language models to activate fewer neurons during inference, constructing structured sparsity to improve inference efficiency.
π― What it does: This paper proposes a Pose-Invariant Implicit Function based on Local 3D Patches (PEIF), which normalizes poses and learns pose-invariant patch representations through a multi-head memory bank, directly predicting the displacement vectors of query points to achieve continuous reconstruction of 3D surfaces.
π― What it does: Using the motion trajectory of a single fabric to learn the constitutive relationships of clothing, and then animating various garments through energy optimization.
π― What it does: A neural symbolic model called NeuRes has been developed for propositional satisfiability (SAT) problems, capable of parallelly generating verifiable unsatisfiability proofs or satisfying assignments, ensuring that the decision results are verifiable.
π― What it does: This paper proposes an advanced contrastive learning framework PCLHD for unsupervised visible-infrared person re-identification, combining hard prototypes and dynamic prototypes to explore the differences between identities and sample diversity.
Learning Cooperative Trajectory Representations for Motion Forecasting
Hongzhi Ruan (Tsinghua University), Zaiqing Nie (Tsinghua University)
CodeAutonomous DrivingGraph Neural NetworkTransformerSupervised Fine-TuningGraphTime Series
π― What it does: The V2X-Graph framework is proposed to achieve graph-based collaborative trajectory feature fusion, enhancing motion prediction performance.
Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios
Shihan Ma (Imperial College London), Jose C Principe
CodeClassificationRecognitionMultimodalityBiomedical Data
π― What it does: Utilizing orthogonal normalized density ratio decomposition (FMCA) to learn multidimensional statistical dependencies from parallel EEG-EMG recordings, extracting feature projection spaces that capture movement types and subject identities;
π― What it does: This paper proposes a data-driven method for selecting cutting planes, providing an upper bound on the sample complexity for choosing the best cutting plane generator function (CGF) family under a given instance distribution, and experimentally verifies its superiority over the traditional GMI cuts.
π― What it does: This paper proposes an unsupervised method called deb LoRA, which alleviates the feature bias of long-tail categories in remote sensing data by training low-rank adaptation layers on a pre-trained base model, thereby improving the performance of minority classes in classification and detection tasks.
CodeOptimizationExplainability and InterpretabilityComputational EfficiencyDiffusion modelBiomedical Data
π― What it does: A JKOnet* model based on the first-order optimal condition of JKO is proposed to learn the energy function of diffusion processes from population data.
π― What it does: A single-step generative model based on free-form flows (M-FFF) is proposed, which can directly learn distributions and sample on any Riemannian manifold.
Learning Formal Mathematics From Intrinsic Motivation
Gabriel Poesia (Stanford University), Noah Goodman
CodeTransformerLarge Language ModelReinforcement Learning
π― What it does: The MINIMO system is proposed, which utilizes a formal mathematical domain given only axioms to simultaneously learn to automatically generate challenging propositions (conjectures) and find their proofs, forming a self-reinforcing training loop.
π― What it does: An improvement of domain generalization semantic segmentation for Vision Foundation Model using frequency adaptive learning methods.
Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks
Eszter Szekely, Sebastian Goldt (International School of Advanced Studies)
CodeTabular
π― What it does: This study investigates the role of higher-order covariances (fourth order and above) in neural network learning, proposing a whitened higher-order peak model and analyzing statistical and computational sample complexity through likelihood ratios and lower-order methods.
Leyan Deng (University of Science and Technology of China), Enhong Chen (University of Science and Technology of China)
CodeRestorationAnomaly DetectionGraph Neural NetworkTime Series
π― What it does: This paper proposes a one-step propagation and confidence refinement (OPCR) method for highly sparse spatiotemporal data, aimed at efficiently recovering missing information and enhancing the performance of subsequent tasks.
π― What it does: A framework based on Conditional Distribution Robust Optimization (CDRO) is proposed, which minimizes extreme risk within a distance-constrained fuzzy set using estimated true label posteriors, and generates robust pseudo-labels through analytical solutions for each sample, ultimately resulting in a robust training method that adaptively adjusts Lagrange multipliers in a single update.
π― What it does: This study proposes a training framework named LOFF-TA, which extracts features from the training set images using a large foundational model in a single pass and caches them, then trains a lightweight classifier on the cached features, utilizing tensor augmentation instead of traditional image augmentation.
π― What it does: By training the teacher model and the student model together, the student's ability to imitate the teacher is used as a regularization signal to enhance the model's generalization performance on unseen data.
Learning from Uncertain Data: From Possible Worlds to Possible Models
Jiongli Zhu (University of California), Babak Salimi (University of Illinois)
CodeOptimizationTabular
π― What it does: Using possible world semantics and abstract interpretation, symbolic gradient descent is applied to uncertain training data to obtain a set of all possible linear models, providing model parameters and prediction intervals for inference.
Learning Identifiable Factorized Causal Representations of Cellular Responses
Haiyi Mao (Genentech), Lin Qiu (Genentech)
CodeRepresentation LearningAuto EncoderBiomedical Data
π― What it does: A recognizable causal decomposition representation (FCR) model is proposed, which can decompose the response of single cells to treatments into independent latent spaces of cellular background, the treatment itself, and their interactions.
π― What it does: The paper proposes a strategy for training diffusion models using only image patches and positional encoding to construct a global image prior (PaDIS), and applies it to solve various inverse problems (CT reconstruction, deblurring, super-resolution).
π― What it does: A two-stage, interaction-aware 3D Gaussian point rendering framework is proposed to quickly construct animatable interactive hand avatars from a single image.
Learning Macroscopic Dynamics from Partial Microscopic Observations
Mengyi Chen (National University of Singapore), Qianxiao Li (National University of Singapore)
CodeAuto EncoderPhysics Related
π― What it does: A method is proposed to learn macroscopic dynamics by utilizing the sparsity assumption and only calculating the forces of a subset of microscopic coordinates.
Learning on Large Graphs using Intersecting Communities
Ben Finkelshtein (University of Oxford), Ron Levie (Technion Israel Institute of Technology)
CodeClassificationComputational EfficiencyGraph Neural NetworkGraphTime Series
π― What it does: This paper proposes a novel graph learning framework - Intercommunity Graph (ICG), which approximates any large graph as a linear combination of several intersecting cliques through a weak regularization lemma, thereby making the memory and time complexity of graph learning algorithms proportional only to the number of nodes.
π― What it does: A self-supervised objective function based on similarity is proposed and validated to learn joint spatial and contextual representations in neural networks, resulting in representations similar to hippocampal place cells, and its remapping behavior is explored.
π― What it does: A self-supervised learning method aimed at 'straightening' has been designed and validated, training the network to produce representations with more linear temporal trajectories in image sequences, thereby achieving predictable and robust visual embeddings.
Learning Representations for Hierarchies with Minimal Support
Benjamin Rozonoyer (University of Massachusetts Amherst), Andrew McCallum (University of Massachusetts Amherst)
CodeOptimizationRepresentation LearningGraph Neural NetworkGraphBiomedical Data
π― What it does: A framework is constructed to determine the unique distinguishing submatrix from known structural constraints (such as transitive closed DAGs) and is used for efficient training of node embeddings.
π― What it does: This paper proposes a simple method to directly learn Successor Features (SF) from pixels, without the need for pre-training or complex auxiliary losses, enabling real-time learning during task execution.
π― What it does: This paper proposes a generative model based on latent causal structures, which automatically learns instance-dependent noise transition matrices using only noisy labeled data and a small number of clean samples, and uses them to correct the training of classifiers.
π― What it does: A reinforcement learning algorithm based on contrastive success representation is developed to train auxiliary agents to maximize human 'effective empowerment' without inferring human rewards.
π― What it does: This paper proposes a method for learning to compute GrΓΆbner bases using Transformers and designs efficient algorithms for the random generation of GrΓΆbner bases and the inverse GrΓΆbner problem, addressing the challenges of dataset generation.
π― What it does: A 3D facial texture reconstruction framework is proposed that utilizes neural representation to separate ambient light, capable of recovering clear textures under unnatural lighting caused by external occlusions.
Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks
Tianyu He (University of Maryland), Andrey Gromov (Meta)
CodeTransformerLarge Language ModelTabular
π― What it does: Exploring the contextual learning and task combination capabilities of modular arithmetic tasks on GPT-style Transformers, and analyzing their impact on O.O.D. generalization.
Learning to Handle Complex Constraints for Vehicle Routing Problems
Jieyi Bi (Nanyang Technological University), Jie Zhang (Nanyang Technological University)
CodeOptimizationReinforcement LearningTabular
π― What it does: A proactive infeasibility prevention (PIP) framework is proposed to assist neural network constructors in generating feasible and high-quality solutions for vehicle routing problems under complex constraints.
Jan van Delden (University of Gttingen), Timo LΓΌddecke
CodeConvolutional Neural NetworkTransformerPhysics Related
π― What it does: This paper proposes and evaluates a new Frequency-Query Operator (FQO) model for predicting the vibration modes and frequency responses of plates under different excitation frequencies by constructing a dataset of 12,000 vibrating plate samples with varying boundaries, materials, and detailed structures (beading).
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios
Shantanu Jaiswal (Carnegie Mellon University), Cheston Tan (Centre for Frontier AI Research)
CodeOptimizationExplainability and InterpretabilityTransformerVision Language ModelImageVideo
π― What it does: A fully neural iterative and parallel reasoning mechanism named IPRM is proposed to handle complex visual question answering scenarios, capable of generating multiple operations in parallel at each reasoning step and updating memory iteratively.
Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way
Ming Chen (National University of Defense Technology), Yingwu Chen (National University of Defense Technology)
CodeOptimizationGraph Neural NetworkBenchmark
π― What it does: A classification-based neural solver VCM is proposed, which utilizes a Deep Value Network (DVN) to extract value features of QUBO, and then outputs a global binary solution in one go through a Value Classification Network (VCN), training the model using a Greedy-guided Self Trainer (GST) without prior optimal labels.
Learning to Understand: Identifying Interactions via the MΓΆbius Transform
Justin Singh Kang (University of California Berkeley), Kannan Ramchandran (University of California Berkeley)
CodeExplainability and InterpretabilityComputational EfficiencyTransformerSupervised Fine-TuningTextTabular
π― What it does: A fully non-adaptive Sparse MΓΆbius Transform (SMT) algorithm is proposed for efficiently reconstructing sparse, low-order interactions in machine learning models, enhancing model interpretability.
Yunqiao Yang (City University of Hong Kong), Ying Wei (Zhejiang University)
CodeMeta LearningTransformerImageBenchmark
π― What it does: A meta-learning based hypernetwork method is proposed for single-sample editing of pre-trained Vision Transformers (ViT), which first locates the parameters to be modified and then fine-tunes them.
π― What it does: A learning layer caching mechanism (Learning-to-Cache) is proposed, which dynamically decides which layers can be cached during the denoising process of the diffusion Transformer, reducing computational load without updating model parameters.
Least Squares Regression Can Exhibit Under-Parameterized Double Descent
Xinyue Li (Yale University), Rishi Sonthalia (Boston College)
CodeSupervised Fine-TuningTabular
π― What it does: Two linear regression models are proposed, which exhibit a bimodal descent phenomenon in the under-parameterized region, and it is proven that the peak positions are influenced by the sample covariance spectrum and the alignment of the target vector with the singular vectors.
Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models
Xu Yang (Southeast University), Hanwang Zhang (Nanyang Technological University)
CodeGenerationRetrievalTransformerLarge Language ModelVision Language ModelImageTextMultimodality
π― What it does: This paper proposes a micro language model named Lever-LM, designed to generate high-quality context demonstration (ICD) sequences for large visual-language models (LVLM) to enhance their reasoning capabilities in visual-language tasks.
π― What it does: This paper proposes BeatDiff, a lightweight diffusion model for generating 12-lead heartbeat morphologies, and designs EM-BeatDiff based on this model to perform multiple ECG processing tasks such as denoising, missing lead reconstruction, and anomaly detection within a Bayesian inverse problem framework without requiring additional training.
Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models
Sadegh Mahdavi (University of British Columbia), Yanshuai Cao (Borealis AI)
CodeTransformerLarge Language ModelReinforcement LearningText
π― What it does: This paper automatically generates PDDL domain and problem files through interaction between a large language model and the environment, completely eliminating human intervention.
CodeRepresentation LearningGraph Neural NetworkImageBiomedical Data
π― What it does: ProtoSurv utilizes heterogeneous graph neural networks combined with pathological prior knowledge to model tumor heterogeneity in whole slide images, achieving cancer survival risk prediction.
Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning
Alex Jinpeng Wang (National University of Singapore), Mike Zheng Shou
CodeRetrievalRepresentation LearningTransformerVision Language ModelContrastive LearningImageTextMultimodality
π― What it does: Proposes the VisInContext method, which utilizes a visual encoder to convert long texts into image-based visual tokens, enabling efficient context length expansion in multimodal large models.
π― What it does: A simple and effective domain generalization framework LFME is proposed, which utilizes expert models for each source domain to guide the target model through logit regularization during the training phase, allowing the target model to perform as an expert across all source domains, thereby achieving better generalization performance on unknown target domains.
LG-CAV: Train Any Concept Activation Vector with Language Guidance
Qihan Huang (Zhejiang University), Mingli Song (Zhejiang University)
CodeClassificationExplainability and InterpretabilityVision Language ModelImage
π― What it does: This paper proposes a method to train arbitrary Concept Activation Vectors (LG-CAV) using concept descriptions provided by the pre-trained vision-language model CLIP, allowing for the generation of high-quality CAVs without the need for extensive manual image labeling, which can then be used for model calibration.
π― What it does: A lightweight and theoretically feasible continuous unbalanced entropy regularized optimal transport (UEOT) solver, called U-LightOT, is proposed;
Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesis
Xin Jin (Nankai University), Bo Ren (Nankai University)
CodeRestorationData SynthesisComputational EfficiencyNeural Radiance FieldGaussian SplattingSimultaneous Localization and MappingImage
π― What it does: Reconstruct HDR scenes from multi-perspective noisy RAW images, achieving real-time rendering and post-processing (exposure changes, HDR gamma mapping, focus transformation, etc.)
π― What it does: In the cross-domain few-shot semantic segmentation task, a lightweight frequency mask (Amplitude-Phase Masker and Adaptive Channel Phase Attention) is introduced to reduce the channel correlation of feature maps, thereby enhancing the model's robustness to inter-domain differences and significantly improving mIoU.
Linear Causal Representation Learning from Unknown Multi-node Interventions
Burak VarΔ±cΔ± (Carnegie Mellon University), Ali Tajer (Rensselaer Polytechnic Institute)
CodeRepresentation LearningGraph
π― What it does: This paper proposes a causal representation learning (CRL) framework for the scenario of unknown multi-node interventions (UMN) and provides the corresponding identifiability theory and implementation algorithm UMNI-CRL.
Linear Uncertainty Quantification of Graphical Model Inference
Chenghua Guo (Beijing University of Posts and Telecommunications), Xi Zhang (Beijing University of Posts and Telecommunications)
CodeOptimizationExplainability and InterpretabilityComputational EfficiencyGraph Neural NetworkGraph
π― What it does: A linear propagation-based uncertainty quantification method, LinUProp, is proposed for graphical model inference, which can quickly and analytically provide the interval width of posterior confidence and ensures convergence and linear scalability.
Linguistic Collapse: Neural Collapse in (Large) Language Models
Robert Wu (University of Toronto), Vardan Papyan (University of Toronto)
CodeGenerationTransformerLarge Language ModelText
π― What it does: This study investigates the phenomenon of neural collapse in autoregressive language models, conducting experiments with GPT-Neo models of varying sizes and training settings on the TinyStories dataset, and quantifying the relationship between four NC attributes and generalization.
π― What it does: A novel point cloud representation learning framework called LinNet is proposed, which employs linear complexity neighborhood search and downsampling, and introduces the Disassembled Set Abstraction (DSA) module to achieve lightweight and efficient local feature aggregation.
π― What it does: Proposes the LipFD method, which utilizes the temporal inconsistency between audio and lip movements to detect Lip-Syncing deepfake videos.
Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
Tiansheng Huang (Georgia Institute of Technology), Ling Liu (Georgia Institute of Technology)
CodeOptimizationSafty and PrivacyTransformerLarge Language ModelSupervised Fine-TuningText
π― What it does: This paper proposes a method to reduce security drift caused by harmful fine-tuning attacks during the fine-tuning phase of large language models (LLMs) through Dual-State Optimization (BSO) and its improved version, Lisa.
LLaMo: Large Language Model-based Molecular Graph Assistant
Jinyoung Park (Korea University), Hyunwoo J. Kim (Korea University)
CodeGenerationDrug DiscoveryGraph Neural NetworkTransformerLarge Language ModelSupervised Fine-TuningTextGraph
π― What it does: LLaMo is proposed, an end-to-end trained large molecular graph language model that can simultaneously perform tasks such as molecular description generation, IUPAC name prediction, and property prediction.
LLM Dataset Inference: Did you train on my dataset?
Pratyush Maini (Carnegie Mellon University), Adam Dziedzic (CISPA Helmholtz Center for Information Security)
CodeSafty and PrivacyAdversarial AttackTransformerLarge Language ModelText
π― What it does: This paper investigates the reasons for the failure of membership inference attacks (MIA) on large-scale language models and proposes a 'dataset inference' method based on multiple MIA aggregations to detect whether a certain dataset has been used for training LLMs.
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
James Requeima (University of Toronto), David Duvenaud (University of Toronto)
CodeLarge Language ModelPrompt EngineeringTabularTime Series
π― What it does: A method called LLM Processes (LLMP) is proposed, which can directly extract numerical prediction distributions from large language models, supporting zero-shot multi-dimensional regression and text conditioning.
LLM-AutoDA: Large Language Model-Driven Automatic Data Augmentation for Long-tailed Problems
Pengkun Wang (University of Science and Technology of China), Yang Wang (University of Science and Technology of China)
CodeData-Centric LearningTransformerLarge Language ModelReinforcement LearningPrompt EngineeringImage
π― What it does: This paper proposes an automated long-tail data augmentation framework based on large language models, LLM-AutoDA, which automatically searches for and generates optimal augmentation strategies for long-tail distributions.
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation
Qidong Liu (Xi'an Jiaotong University), Xiangyu Zhao (City University of Hong Kong)
CodeRecommendation SystemRecurrent Neural NetworkTransformerLarge Language ModelSequentialRetrieval-Augmented Generation
π― What it does: This paper proposes an enhanced framework using large language model (LLM) embeddings (LLM-ESR) that improves the performance of sequential recommendation systems for long-tail users and long-tail items through dual-view modeling and retrieval-augmented self-distillation.
LLMDFA: Analyzing Dataflow in Code with Large Language Models
Chengpeng Wang (Purdue University), Xiangyu Zhang (Purdue University)
CodeAI Code AssistantTransformerLarge Language ModelPrompt EngineeringTextChain-of-Thought
π― What it does: A customizable data flow analysis framework LLMDFA is implemented using large language models (LLM) without the need for compilation;
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
Duo Wang (Beihang University), Junjie Wu (Beihang University)
CodeClassificationRepresentation LearningGraph Neural NetworkLarge Language ModelContrastive LearningGraph
π― What it does: The TEA-GLM framework is proposed, utilizing LLM as a zero-shot graph learner across datasets and tasks, aligning GNN representations with LLM token embeddings, and generating a fixed number of graph token embeddings through linear projection, combined with unified instructions to complete graph tasks such as node classification and link prediction.
LLMs Can Evolve Continually on Modality for $\mathbb{X}$-Modal Reasoning
Jiazuo Yu (Dalian University of Technology), Long Chen (The Hong Kong University of Science and Technology)
CodeLarge Language ModelMixture of ExpertsImageVideoMultimodalityPoint CloudBenchmarkAudio
π― What it does: A flexible and scalable framework named PathWeave is proposed, enabling multimodal large language models (MLLMs) to continuously evolve in multimodal reasoning.
π― What it does: A learnable multi-layer isochronous threshold model (LM-HT) is proposed, achieving performance comparable to quantized ANN under extremely low latency through global time-weighted membrane potential and learnable thresholds, and providing a scheme for lossless conversion to a single-threshold LIF model.
π― What it does: In federated learning, pre-trained models are utilized to reduce communication rounds and enhance global model performance through Local Superior Soups (LSS) via local model interpolation.
π― What it does: This paper studies the learning dynamics of a single-layer Transformer on first-order Markov chain data, clarifying how initialization and data switching factors determine convergence to global or local minima.
Localize, Understand, Collaborate: Semantic-Aware Dragging via Intention Reasoner
Xing Cui (Beijing University of Posts and Telecommunications), Zhaofeng He (University of California Santa Barbara)
CodeImage TranslationLarge Language ModelVision Language ModelDiffusion modelImageBenchmark
π― What it does: This paper proposes a drag-and-drop editing framework named LucidDrag, which enables semantic-aware and controllable image editing based on user-specified drag points in images.
Matteo Zecchin (King's College London), Osvaldo Simeone (King's College London)
CodeSegmentationOptimizationImageTime Series
π― What it does: Proposed and implemented Localized Adaptive Risk Control (L-ARC), an online calibration method that achieves local risk control of the prediction set by learning variable threshold functions in RKHS.
Wenyang Hu (National University of Singapore), Bryan Kian Hsiang Low (National University of Singapore)
CodeOptimizationLarge Language ModelPrompt EngineeringText
π― What it does: This paper proposes a prompt optimization algorithm ZOPO based on local zero-order optimization, aimed at efficiently searching for locally optimal prompts on black-box LLMs;
LoFiT: Localized Fine-tuning on LLM Representations
Fangcong Yin (University of Texas at Austin), Greg Durrett (University of Texas at Austin)
CodeOptimizationExplainability and InterpretabilityTransformerLarge Language ModelSupervised Fine-TuningText
π― What it does: A local fine-tuning method named LOFIT is proposed, which first locates the most important attention heads for specific tasks through learning scaling factors, and then learns the bias vectors of the representations of these heads, achieving efficient tuning of large language models;
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
Otmane Sakhi (Criteo AI Lab), Nicolas Chopin (CREST)
CodeOptimizationReinforcement LearningTabular
π― What it does: A logarithmic smoothing-based IPS estimator (Logarithmic Smoothing, LS) is proposed, and lazy lower bound estimation, policy selection, and learning are implemented in an offline context;
CodeTransformerLarge Language ModelAgentic AITextRetrieval-Augmented Generation
π― What it does: The LongFact dataset and SAFE evaluation method are proposed to measure the factual accuracy of large language models in long texts.
Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments
Siddharth Nayak (Massachusetts Institute of Technology), Hamsa Balakrishnan (Massachusetts Institute of Technology)
CodeRobotic IntelligenceTransformerLarge Language ModelVision Language ModelMultimodalityBenchmark
π― What it does: A multi-agent long-term planning framework based on language models, LLaMAR, is proposed, which can dynamically plan, correct, and verify subtask execution in partially observable environments.
π― What it does: This paper proposes a brain map Transformer named ALTER, specifically designed to capture long-distance dependencies between brain regions and integrate them with short-distance information, enhancing the diagnostic performance of neurological diseases.
π― What it does: This study investigates the dynamic and static representations of the visual cortex under natural movie stimuli and proposes a long-range feedback pulse neural network (LoRaFB-SNet) that simulates top-down feedback and pulse mechanisms to more closely align with the neural activity of the mouse visual cortex.
π― What it does: This paper proposes an automated framework for searching long-range meta-paths, called LMSPS, which utilizes evolutionary sampling and sampling evaluation to select effective meta-paths on large-scale heterogeneous graphs, thereby enhancing the effectiveness of graph representation learning.