π― What it does: This paper proposes a scalable automated content-style-stylization image triplet construction pipeline and builds a large-scale dataset IMAGStyle with 210K samples based on this pipeline. An end-to-end CSGO framework is then trained on this dataset, achieving image-driven, text-driven, and text-editing-driven style transfer.
π― What it does: This study investigates the detection of contraband based on X-ray images and proposes a Category Semantic Prior Contrastive Learning mechanism (CSPCL), which enhances the model's robustness against interference from overlapping features by aligning classifier weights (category prototypes) with content queries in the decoder.
π― What it does: A neural symbolic learning framework named CTSketch is proposed, which achieves differentiable inference for large-scale symbolic reasoning by decomposing symbolic programs into subprograms and performing low-rank tensor compression on the tensor summaries of each subprogram.
π― What it does: A CURE framework is proposed, utilizing reinforcement learning to achieve the co-evolution of the encoder and unit test generator without requiring real code as supervision;
Curl Descent : Non-Gradient Learning Dynamics with Sign-Diverse Plasticity
Hugo Ninou (Ecole Normale Superieure PSL), N Alex Cayco Gajic
CodeOptimizationTabular
π― What it does: This paper systematically studies the naturally occurring non-gradient 'curl' terms in biologically plausible synaptic plasticity rules (such as excitatory-inhibitory networks or mixed Hebbian/anti-Hebbian rules) and proposes a 'curl descent' learning ruleβintroducing the curl term by reversing the signs of some synaptic updates. The authors perform analytical stability analysis in a two-layer linear student-teacher framework, utilize random matrix theory to plot phase diagrams under different network architectures and curl strengths, and validate through numerical simulations the phenomena of chaotic learning, instability, and accelerated convergence caused by the curl term.
π― What it does: A new method called Curly Flow Matching (CURLY-FM) is proposed for learning non-gradient field dynamics, capturing periodic behavior by solving the SchrΓΆdinger bridge problem with a non-zero drift reference process.
Curriculum Design for Trajectory-Constrained Agent: Compressing Chain-of-Thought Tokens in LLMs
Georgios Tzannetos (Max Planck Institute for Software Systems), Adish Singla (Max Planck Institute for Software Systems)
CodeOptimizationComputational EfficiencyTransformerLarge Language ModelReinforcement LearningTextChain-of-Thought
π― What it does: An adaptive constraint adjustment strategy based on curriculum learning is proposed, allowing reinforcement learning and large language models to gradually tighten constraints under strict trajectory constraints and ultimately meet hard constraints during deployment; validation is conducted on binary tree MDP, PuddleGrid, and mathematical reasoning tasks.
CURV: Coherent Uncertainty-Aware Reasoning in Vision-Language Models for X-Ray Report Generation
Ziao Wang (Hong Kong Baptist University), William K. Cheung (Hong Kong Baptist University)
CodeGenerationTransformerSupervised Fine-TuningReinforcement LearningVision Language ModelTextMultimodalityBiomedical DataElectronic Health Records
π― What it does: The CURV framework is proposed, utilizing a vision-language model to generate chest X-ray reports, incorporating structured uncertainty expression and an explicit reasoning process (Findings-Thinking-Impression) in the reports.
π― What it does: A Curvature Tuning (CT) method is proposed to achieve model Steering by injecting a single hyperparameter into the activation function, which can serve as untrained Steering (S-CT) or as a trainable parameter-efficient Fine-Tuning (T-CT).
Cycle-Sync: Robust Global Camera Pose Estimation through Enhanced Cycle-Consistent Synchronization
Shaohan Li (University of Minnesota), Gilad Lerman (University of Minnesota)
CodePose EstimationSimultaneous Localization and MappingImage
π― What it does: Proposes the Cycle-Sync method, constructing a global and robust camera pose estimation framework that can simultaneously estimate camera rotation and translation without the need for traditional bundle adjustment steps.
π― What it does: A cyclic information bottleneck space (CyIN) has been constructed, capable of handling both complete and incomplete multimodal learning, and completing missing modalities through cross-modal cyclic translation.
π― What it does: This study investigates the use of sketches and pseudo-labeled satellite images to generate 3D outdoor semantic scenes, proposing the CymbaDiff diffusion model and the SketchSem3D dataset.
π― What it does: A cross-staining virtual staining method D-VST based on diffusion Transformer is proposed, which achieves controllable staining by adjusting the color tone while maintaining the accuracy of pathological structures.
π― What it does: A trainable Discrepancy-Amplifying Adapter (DAA) and Short-Term Memory Renewal (STMR) mechanism are proposed to achieve real-time adaptation to unknown categories during Test-Time Discovery while maintaining performance on known categories.
π― What it does: The DAAC framework is proposed, which enhances the representation learning and generalization ability of medical time series diagnostic models through a difference estimator generated from external normal samples and multi-view adaptive contrastive learning.
π― What it does: The DAIL framework is proposed, which reduces task ambiguity caused by language instructions through distributed value estimation and trajectory semantic alignment, achieving more accurate task identification and execution.
π― What it does: A dynamic adaptive scanning-based visual state space model called DAMamba is proposed to enhance the performance of image classification, object detection, instance segmentation, and semantic segmentation.
DartQuant: Efficient Rotational Distribution Calibration for LLM Quantization
Yuantian Shao (Nanjing University of Science and Technology), Jian Cheng (Chinese Academy of Sciences)
CodeOptimizationComputational EfficiencyTransformerLarge Language ModelText
π― What it does: DartsQuant is proposed, a distribution calibration-based rotation matrix quantization method that efficiently completes activation quantization of large-scale language models without the need for end-to-end fine-tuning.
Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
Xiao Han (Zhejiang University of Technology), Xiangyu Zhao (City University of Hong Kong)
CodeDomain AdaptationData-Centric LearningTransformerLarge Language ModelSupervised Fine-TuningText
π― What it does: A continuous low-rank fine-tuning framework named DEAL is proposed for efficient and continuous adaptation on large language models.
π― What it does: A partial identification framework is proposed for situations where both the experimental group and the observational group simultaneously violate the assumptions of no unobserved confounding and research exchangeability, providing interpretable sensitivity parameters Ξ³ and Ο to quantify the violations of assumptions, and subsequently deriving upper and lower bounds for treatment effects.
Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework
Thomson Yen (Columbia Business School), Hongseok Namkoong (Columbia Business School)
CodeOptimizationLarge Language ModelText
π― What it does: This paper proposes a multi-fidelity multi-scale Bayesian optimization framework for automatically optimizing the training data mixing ratio during the pre-training process of large language models.
Data Selection Matters: Towards Robust Instruction Tuning of Large Multimodal Models
Xu Yang (City University of Hong Kong), Ying Wei (Zhejiang University)
CodeData-Centric LearningTransformerSupervised Fine-TuningVision Language ModelImageTextMultimodality
π― What it does: A gradient-free robustness-oriented data selection framework (ARDS) has been constructed for visual instruction tuning, selecting a training subset that can enhance the robustness of multimodal models against positional bias and spurious correlations.
π― What it does: This study investigates model extraction attacks on recommendation systems in a black-box, no-data environment and proposes a data-free model extraction method based on graph convolution (DBGRME).
DBLoss: Decomposition-based Loss Function for Time Series Forecasting
Xiangfei Qiu (East China Normal University), Bin Yang (East China Normal University)
CodeTransformerTime Series
π― What it does: A loss function called DBLoss based on time series decomposition is proposed, which uses EMA to decompose the predicted values and true values into seasonal and trend components within the prediction interval, and calculates the loss separately before summing them with weights;
π― What it does: This paper proposes an adaptive density control method based on Direction Consistency (DC) called DC4GS, aimed at improving the original splitting and sub-primitive position selection in 3D Gaussian splatting, thereby reducing the number of Gaussians and enhancing rendering quality without increasing computational load.
π― What it does: A graph-guided deep embedding clustering framework DCA is proposed for generating personalized and voxel-level brain region partitions.
π― What it does: A Dual-Conditional Inversion method is designed, which combines text prompts and reference images to guide the reverse process of the diffusion model, aiming to obtain more accurate and editable latent noise.
Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?
Hyeong Kyu Choi (University of Wisconsin Madison), Sharon Li (University of Wisconsin Madison)
CodeTransformerLarge Language ModelAgentic AIText
π― What it does: This paper studies Multi-Agent Debate (MAD) systems, breaking it down into two parts: voting and debate. It conducts large-scale experiments and theoretical analysis, proving that majority voting is the main source of improvement, while the debate itself does not increase accuracy in expectation, and proposes improvement strategies.
Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning
Danni Yang (Tsinghua University), Mingming Gong (University of Melbourne)
CodeFederated LearningKnowledge DistillationImage
π― What it does: This paper proposes a decentralized dynamic collaboration framework for personalized federated continual learning, allowing clients to dynamically form non-overlapping cooperative alliances at each task stage based on the trade-off between knowledge acquisition and retention of previous learning, thereby mitigating catastrophic forgetting.
Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation
Liliang Ren (Microsoft), yelong shen
CodeGenerationComputational EfficiencyTransformerLarge Language ModelTextRetrieval-Augmented Generation
π― What it does: This paper proposes the Gated Memory Unit (GMU) and designs the SambaY decoder-hybrid-decoder architecture based on it to significantly improve the decoding efficiency of long sequence inference.
DecompNet: Enhancing Time Series Forecasting Models with Implicit Decomposition
Donghao Luo (Tsinghua University), Xue Wang (Tsinghua University)
CodeTransformerMixture of ExpertsTime Series
π― What it does: The concept of Implicit Decomposition is proposed, and based on this idea, the DecompNet framework is constructed, which can inject seasonal and trend knowledge into the model without explicitly decomposing the input sequence, achieving performance improvement with no additional inference cost.
Jing Ma (Huazhong University of Science and Technology), Xiang Xiang (Huazhong University of Science and Technology)
CodeDomain AdaptationReinforcement LearningImage
π― What it does: A new self-supervised entropy minimization method called AdaDEM is proposed, which decouples and improves traditional entropy minimization, addressing the issues of reward collapse and class bias.
Decoupling Contrastive Decoding: Robust Hallucination Mitigation in Multimodal Large Language Models
Wei Chen (Hong Kong University of Science and Technology), Long Chen (Hong Kong University of Science and Technology)
CodeOptimizationReinforcement Learning from Human FeedbackTransformerLarge Language ModelReinforcement LearningVision Language ModelContrastive LearningMultimodality
π― What it does: A decoupled contrastive decoding framework (DCD) is proposed, which suppresses the hallucinations of multimodal large models by separately learning the projections of positive and negative samples and comparing positive and negative visual features during inference.
π― What it does: A scalable 'Compositional Phase Diffusion' framework is proposed to generate long sequences, composite, and intermediate human actions, achieving smooth transitions between segments.
π― What it does: Proposes the Deep Edge Filter, which utilizes the high-frequency components extracted from deep features after low-pass filtering to enhance the model's generalization ability.
Aleksey Minabutdinov (ETH Zurich), Patrick Cheridito (ETH Zurich)
CodeOptimizationConvolutional Neural Network
π― What it does: Proposes the Deep Legendre Transform (DLT) method, which uses deep learning to compute the Legendre transform (convex conjugate) of differentiable convex functions.
π― What it does: This paper presents ForageWorld, a naturalistic open-ended reinforcement learning environment, and combines tools from neuroscience and animal behavior to jointly analyze the behavior and internal representations of deep RL agents, revealing their implicit planning and memory capabilities.
Chang Nie (Nanjing University of Science and Technology)
CodeClassificationRecommendation SystemOptimizationConvolutional Neural NetworkTransformerImagePhysics Related
π― What it does: A deep tree tensor network (DTTN) is designed and implemented, which achieves exponential-order feature interaction through stacking anti-symmetric interaction modules (AIM) without the need for activation functions, and is applied to image classification, recommendation, and solving physical problems.
Deep Value Benchmark: Measuring Whether Models Generalize Deep values or Shallow Preferences
Joshua Ashkinaze (University of Michigan), Ceren Budak (University of Michigan)
CodeLarge Language ModelPrompt EngineeringTextBenchmarkChain-of-Thought
π― What it does: This study investigates whether large language models learn deep human values or merely memorize surface preferences, and proposes the Deep Value Benchmark (DVB) to directly measure the models' generalization ability regarding deep values.
Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding
Xiaoyi Zhang (Microsoft Research), Yan Lu (Microsoft Research)
CodeRecognitionRetrievalTransformerLarge Language ModelAgentic AIVision Language ModelVideoText
π― What it does: Proposed and implemented the Deep Video Discovery (DVD) agent, which utilizes a multi-granularity video database and toolchain to achieve autonomous search and reasoning for long videos.
Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
Li Sun (Beijing University of Posts and Telecommunications), Philip S. Yu (University of Illinois)
CodeClassificationGraph Neural NetworkGraph
π― What it does: Proposes a non-homogeneous boundary condition based on local Riemannian geometry to simultaneously address the issues of over-smoothing and over-compression in graph message passing networks;
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO
Jinyoung Park (Korea Advanced Institute of Science and Technology), Hyunwoo J. Kim (Korea Advanced Institute of Science and Technology)
CodeLarge Language ModelReinforcement LearningVideoBenchmark
π― What it does: A video large language model DeepVideo-R1 is proposed, which uses regression GRPO (Reg-GRPO) and difficulty-aware data augmentation for video reinforcement fine-tuning to enhance video reasoning capabilities.
π― What it does: A rejection-based training method is proposed, utilizing a small number of clean samples to defend against backdoor-infected CLIP models.
Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts
Andrea Pugnana (University of Trento), Davide Bacciu (University of Pisa)
CodeExplainability and InterpretabilityConvolutional Neural NetworkImage
π― What it does: Designed and trained Deferring Concept Bottleneck Models (DCBMs), enabling traditional concept bottleneck models to automatically determine when to request intervention from human experts and when to make predictions independently, thus achieving interpretable human-machine collaboration.
Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs
Jie Ma (Xi'an Jiaotong University), su zhou
CodeRetrievalOptimizationKnowledge DistillationTransformerLarge Language ModelSupervised Fine-TuningGraph
π― What it does: A trustworthy reasoning framework DP is proposed, which enhances the answer credibility of large language models in KG semantic retrieval and multi-hop reasoning by utilizing the structural prior and constraint prior of knowledge graphs.
DeltaFormer: Unlock the state space of Transformer
Mingyu Xu, Shu Zhong
CodeTransformerText
π― What it does: The DeltaFormer model is proposed, which combines the Delta rule with kernel functions to construct a model that can overcome the representation limitations of Transformer TC0.
CodeOptimizationData-Centric LearningTabularPhysics Related
π― What it does: This paper proposes a framework called DeltaPhi that transforms the task of solving PDEs from direct mapping to learning the residuals of similar physical states. It utilizes residual learning to achieve implicit data augmentation, significantly enhancing the performance of neural operators in data-scarce scenarios.
Delving into Large Language Models for Effective Time-Series Anomaly Detection
Junwoo Park (KAIST), Jaewoong Cho (KAIST)
CodeAnomaly DetectionTransformerLarge Language ModelPrompt EngineeringTime Series
π― What it does: This paper discusses the shortcomings of large language models (LLMs) in time series anomaly detection (TSAD) and proposes a zero-shot method that combines statistical decomposition with index-aware prompts.
π― What it does: The paper systematically compares and analyzes the in-domain and out-of-domain performance of GRPO and DPO in autoregressive image generation tasks, and studies the impact of reward models and scaling strategies on the two RL algorithms.
Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning
Chen Qian (Renmin University of China), Jing Shao (Shanghai Artificial Intelligence Laboratory)
CodeTransformerLarge Language ModelTextChain-of-Thought
π― What it does: This study investigates the internal reasoning processes of large reasoning models, proposing the phenomenon of mutual information peak and proving its correlation with reasoning accuracy.
π― What it does: A pruning preconditioning method called DenoiseRotator is proposed, which centralizes parameter importance through learning orthogonal transformations, enhancing the robustness of LLM pruning.
π― What it does: By combining events triggered by focal sweeping and images, an Event Differential Focal Volume (EDFV) is constructed to predict sparse depth, which is then integrated with a single image foundation model (IFM) through a prompting network to obtain high-quality full dense metric depth.
Density Ratio-Free Doubly Robust Proxy Causal Learning
Bariscan Bozkurt (University College London), Arthur Gretton (DeepMind)
CodeImageTabular
π― What it does: Two types of doubly robust kernel estimators (DRKPV and DRPMMR) are proposed within the framework of Proxy Causal Learning, capable of estimating dose-response curves without the need for explicit density ratio estimation, suitable for continuous or high-dimensional treatment variables.
DePass: Unified Feature Attributing by Simple Decomposed Forward Pass
Xiangyu Hong (Tsinghua University), Bowen Zhou (Tsinghua University)
CodeGenerationExplainability and InterpretabilityTransformerLarge Language ModelText
π― What it does: DePass is proposed, a method for achieving interpretability of the Transformer mechanism through single decomposition forward propagation.
Dependency Matters: Enhancing LLM Reasoning with Explicit Knowledge Grounding
Xiangyu Wen (Chinese University of Hong Kong), Qiang Xu (Chinese University of Hong Kong)
CodeTransformerLarge Language ModelSupervised Fine-TuningText
π― What it does: A reasoning framework called GRiD based on a knowledge dependency graph is proposed, which explicitly extracts knowledge from the internal knowledge of LLMs and constructs a graph structure of knowledge and reasoning nodes, combined with a lightweight verifier to ensure logical consistency at each step.
Dependency Parsing is More Parameter-Efficient with Normalization
Paolo Gajo (University of Bologna), Alberto BarrΓ³n-CedeΓ±o (University of Bologna)
CodeRecurrent Neural NetworkGraph
π― What it does: The study and verification of normalizing (scaling) the biaffine scores in dependency parsing can improve model performance and reduce parameters.
π― What it does: This paper proposes a deep supervision-based image stitching method that achieves precise alignment and seamless stitching in large parallax scenes through two-stage depth-aware transformation estimation and flexible seam fusion.
π― What it does: This paper proposes an attack method for stereo depth estimation systems that can be deployed in physical environmentsβDepthVanish, which creates a 'vanishing' attack by jointly optimizing texture elements and spacing structures;
π― What it does: Using an event camera and camera pose to generate a 3D ray density map (DSI), the lightweight deep learning network DERD-Net predicts pixel-level depth, supporting both monocular and binocular scenes.
π― What it does: A no fine-tuning, non-differentiable inference algorithm SVDD is proposed, which guides the diffusion model with a soft value function to optimize downstream rewards.
Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference
Zichen Wang (University of Illinois Urbana-Champaign), Huazheng Wang (Oregon State University)
CodeOptimizationReinforcement Learning from Human FeedbackGraph
π― What it does: This paper studies the multi-armed bandit problem with network interference (MABNI), constructs the theoretical Pareto front in adversarial environments, and proposes the EXP3-N-CS algorithm that can achieve continuous inference while reducing cumulative regret.
DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization
Hongshu Guo (South China University of Technology), Yue-Jiao Gong (South China University of Technology)
CodeOptimizationTransformerLarge Language ModelReinforcement LearningTabular
π― What it does: DesignX is proposed, an automatic black-box optimizer design framework based on dual-agent reinforcement learning, capable of generating human-level optimizers for any black-box problem within seconds.
π― What it does: A generative image detection framework called ConV is proposed, which verifies the consistency of natural image distributions. It constructs two functions to achieve consistency detection using the gradient orthogonality principle of self-supervised models, and actively amplifies the distribution differences between generated images and natural images through regularization flow.
Detecting High-Stakes Interactions with Activation Probes
Alex McKenzie (LASR Labs), Dmitrii Krasheninnikov (University of Cambridge)
CodeAnomaly DetectionComputational EfficiencyTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringText
π― What it does: This paper proposes and evaluates activation probes for detecting high-stakes situations in interactions with large language models, and validates their feasibility in real-world scenarios.
DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models
Simone Carnemolla (University of Catania), Concetto Spampinato (University of Catania)
CodeExplainability and InterpretabilityTransformerLarge Language ModelPrompt EngineeringDiffusion modelImageText
π― What it does: The DEXTER framework is proposed, utilizing diffusion models and large language models to generate global text explanations in the absence of data, aimed at revealing the decision mechanisms and biases of visual classifiers.
π― What it does: DGSolver is designedβa high-order ODE solver implemented on a general diffusion model, specifically for various image restoration tasks combined with general posterior sampling.
π― What it does: Designed and trained a fully convolutional diffusion model DiCo, replacing self-attention with convolution and enhancing performance through compact channel attention, significantly improving computational efficiency.
DiCoFlex: Model-Agnostic Diverse Counterfactuals with Flexible Control
Oleksii Furman (WrocΕaw University of Science and Technology), Marek Εmieja (Jagiellonian University)
CodeExplainability and InterpretabilityComputational EfficiencyFlow-based ModelTabular
π― What it does: A model-free framework DiCoFlex has been designed and implemented to generate diverse and interpretable adversarial explanations with a single forward inference.
DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
Jiaqi Xue (University of Central Florida), Qian Lou (University of Central Florida)
CodeFederated LearningSafty and PrivacyComputational EfficiencyTransformerImageText
π― What it does: The DictPFL framework is proposed, which splits model weights into a static dictionary and a learnable lookup table, encrypting only the lookup table to achieve efficient HE federated learning.
π― What it does: Evaluated and demonstrated that Diffusion-based Purification (DBP) lacks robustness in the presence of precise gradients, and proposed methods such as the DiffBreak tool, DiffGrad gradient implementation, and low-frequency attacks;
Jincheng Zhou (Purdue University), Bruno Ribeiro (Purdue University)
CodeOptimizationGraph Neural NetworkGraph
π― What it does: A differentiable d-separation framework is proposed, and based on this framework, DAGPA is implemented for causal structure learning, which can directly utilize the conditional independence information in the observed data for gradient optimization.
Differentiable Decision Tree via "ReLU+Argmin" Reformulation
Qiangqiang Mao (University of British Columbia), Yankai Cao (University of British Columbia)
CodeClassificationOptimizationExplainability and InterpretabilityTabular
π― What it does: A differentiable slope decision tree based on ReLU+Argmin is proposed, utilizing gradient methods for one-time optimization of the entire tree, achieving a high-precision and interpretable model.
π― What it does: A differentiable generalized sliced Wasserstein plan (DGSWP) is proposed, achieving OT approximation in high dimensions and manifolds through neural network projection and Stein smoothing;
π― What it does: This paper proposes a differentiable hierarchical visual segmenter (βHT) that can adaptively generate visual tokens with pixel-level accuracy in end-to-end training.
π― What it does: In the federated learning environment, the FedASK framework is proposed, using a two-stage sketching technique to achieve differential privacy secure updates for LoRA low-rank adapters;
Differentially Private High-dimensional Variable Selection via Integer Programming
Petros Prastakos (Massachusetts Institute of Technology), Rahul Mazumder (Massachusetts Institute of Technology)
CodeOptimizationSafty and PrivacyTabular
π― What it does: This paper proposes two scalable pure differential privacy variable selection algorithms (topR and mistakes) for support recovery in sparse high-dimensional learning;
Differentially Private Quantiles with Smaller Error
Jacob Imola (University of Copenhagen), Rasmus Pagh (University of Copenhagen)
CodeSafty and PrivacyComputational EfficiencyTabular
π― What it does: A new mechanism for estimating multiple quantiles under pure differential privacy and approximate differential privacy is proposed, utilizing continuous counting techniques to achieve lower rank error.
Differentially Private Relational Learning with Entity-level Privacy Guarantees
Yinan Huang (Georgia Institute of Technology), Pan Li (Georgia Institute of Technology)
CodeSafty and PrivacyGraph Neural NetworkSupervised Fine-TuningTextGraph
π― What it does: This paper proposes a differential privacy training framework suitable for relational learning, providing entity-level DP guarantees.
π― What it does: A general, plug-and-play differentiable layer (dQP) is proposed, which can be used with any black-box quadratic programming (QP) solver, allowing for the simultaneous computation of solutions and gradients with just one KKT matrix decomposition.
DiffEye: Diffusion-Based Continuous Eye-Tracking Data Generation Conditioned on Natural Images
Ozgur Kara (University of Illinois), James Matthew Rehg
CodeGenerationData SynthesisDiffusion modelImage
π― What it does: A framework for generating eye movement trajectories based on diffusion models, called DiffEye, has been designed and implemented. It can generate eye movement trajectories, scanning paths, and saliency maps for natural images by training on raw continuous eye movement trajectories.
π― What it does: In an unsupervised scenario, synthetic images with bias alignment are generated using a conditional diffusion model, training a Bias Amplifier, which is then used as a plugin for debiasing learning in image classification models.
π― What it does: A dynamic text embedding update method called DATE is proposed, which can adjust text embeddings in real-time during the sampling process of diffusion models to better match the generated images;
π― What it does: A synthetic data generation framework DfD for parameter-free model synchronization in federated learning is designed, utilizing inference from local diffusion models instead of parameter exchange, and sampling from local distribution mixtures through ULA.
π― What it does: In this paper, the authors propose a new diffusion process based on score matching, modeling directly in the representation space of arbitrary Lie groups. They derive a discretized Langevin dynamics using generalized score matching and prove that these dynamics are solutions to a dual stochastic differential equation system that can be solved analytically.
π― What it does: This study proposes using the pre-trained diffusion model itself as a noise-aware latent reward model (LRM) and directly performing stepwise preference optimization (LPO) in the noise latent space, significantly improving image quality and training efficiency.
Diffusion Transformers as Open-World Spatiotemporal Foundation Models
Yuan Yuan (Tsinghua University), Yong Li (Tsinghua University)
CodeTransformerPrompt EngineeringDiffusion modelGraphTime Series
π― What it does: This paper presents UrbanDiT, an open-world spatiotemporal foundation model based on diffusion Transformers, capable of uniformly handling various spatiotemporal data such as grids and graphs, and supporting multiple tasks including forward prediction, reverse prediction, temporal interpolation, spatial extrapolation, and spatiotemporal filling in a single pass.
Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification
Zeqi Ye (Northwestern University), Minshuo Chen (Northwestern University)
CodeTransformerDiffusion modelTime Series
π― What it does: This paper studies the statistical efficiency of Diffusion Transformers in time series missing value imputation and uncertainty quantification, providing upper bounds on sample complexity and confidence interval construction, and proposing a mixed masking training strategy.
Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation
Jeongin Kim (Ewha Womans University), Junhyug Noh (Ewha Womans University)
CodeSegmentationDiffusion modelImage
π― What it does: A two-stage active learning framework for low-budget semantic segmentation is proposed, which first selects diverse candidate pixels from multi-scale features extracted from a pre-trained diffusion model using MaxHerding, and then selects the final labeled pixels using eDALD (mutual information + entropy);
π― What it does: A label-agnostic diffusion-based graph data augmentation framework D-GDA is proposed, which generates diverse and consistent incremental samples in the latent space using a graph variational autoencoder and a latent diffusion model.
CodeRecommendation SystemReinforcement Learning from Human FeedbackTransformerSupervised Fine-TuningReinforcement LearningTabular
π― What it does: This paper studies how to obtain a unified strategy that accommodates multiple user types by directly aligning preference data in scenarios where user preferences are highly heterogeneous.
π― What it does: This study investigates a technique for achieving maximum likelihood estimation through local Fisher score matching (FSM) in situations where the likelihood cannot be analytically resolved in likelihood-free inference.
π― What it does: This paper proposes the DISCO method, which utilizes a pre-constructed discrete noise codebook to replace continuous conditional adjustments, achieving conditional generation in text-to-image diffusion models.