Conference on Neural Information Processing Systems Β· 1874 papers
Fair Kernel K-Means: from Single Kernel to Multiple Kernel
Peng Zhou (Anhui University), Liang Du (Shanxi University)
CodeOptimizationTabularBenchmark
π― What it does: A new fairness regularization term is introduced within the framework of Kernel K-Means and Multiple Kernel K-Means, leading to the development of Fair Kernel K-Means (FKKM) and its multi-kernel version (Fair Multiple Kernel K-Means, FMKKM), achieving a balance between fairness in clustering results and clustering quality.
Fairness in Social Influence Maximization via Optimal Transport
Shubham Chowdhary (ETH Zurich), Florian Dorfler
CodeOptimizationReinforcement LearningGraphPhysics Related
π― What it does: This paper proposes a new fairness metricβMutual Fairnessβfor information dissemination in social networks, and based on this metric, designs a seed selection algorithm S3D that balances fairness and efficiency, significantly improving the fairness of information coverage among multiple groups.
π― What it does: This paper proposes a fair graph model (Gaussian, Covariance, Ising) estimation framework, which introduces pairwise graph disparity error and combines it with a custom loss to construct a non-smooth multi-objective optimization aimed at eliminating bias caused by protected attributes.
π― What it does: A training-independent inference framework named Streamlined Inference is proposed, which includes three core modules: Feature Slicer, Operator Grouping, and Step Rehash. It can significantly reduce peak memory usage and computational overhead without compromising video quality.
π― What it does: Proposed a Fast Graph Sharpness-Aware Minimization (FGSAM/FGSAM+) to accelerate and enhance the generalization performance of few-shot node classification.
π― What it does: A fast conditional sampling framework called Conditional Conjugate Integrators is designed, which does not require retraining and can be directly applied to pre-trained diffusion or flow models for linear inverse problems (such as super-resolution, deblurring, and inpainting).
π― What it does: A fast training-to-testing (Fast T2T) model based on optimized consistency is proposed, which can directly generate high-quality combinatorial optimization solutions from noise in a single step or a few steps.
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification
Kexue Fu (Qilu University of Technology), Manning Wang (Fudan University)
CodeClassificationTransformerPrompt EngineeringVision Language ModelImageBiomedical Data
π― What it does: A dual-layer few-shot learning paradigm called FAST is proposed to address the high labeling costs and data scarcity issues in whole slide image (WSI) classification.
Faster Differentially Private Top-$k$ Selection: A Joint Exponential Mechanism with Pruning
Hao WU (University of Waterloo), Hanwen Zhang (University of Copenhagen)
CodeOptimizationSafty and PrivacyComputational EfficiencyTabular
π― What it does: This study investigates the Top-k selection problem under differential privacy and proposes an improved algorithm aimed at identifying the k highest-scoring items from d items.
π― What it does: A general framework based on local diffusion processes is proposed, which can transform graph diffusion equations (such as Personalized PageRank, Katz centrality, Heat kernel) into local iterative solvers, significantly reducing computational costs.
π― What it does: Two types of efficient CUDA kernels are proposed for Neighborhood Attention and multi-dimensional sliding window attention: the GEMM NA kernel based on batch GEMM and the Fused NA kernel, which are integrated into the NATTEN library.
π― What it does: A topic model named FASTopic is proposed, utilizing Dual Semantic-relation Reconstruction (DSR) and Embedding Transport Plan (ETP) to achieve efficient, stable, and transferable topic modeling.
Jonathan So (University of Cambridge), Richard E. Turner (University of Cambridge)
CodeOptimizationTabular
π― What it does: This paper proposes two new variants of Expectation Propagation (EP) - EPΞ· and EPΒ΅, which maintain numerical stability when using Monte Carlo (MC) sampling for estimation updates and can converge efficiently with just a single sample.
FedAvP: Augment Local Data via Shared Policy in Federated Learning
Minui Hong (Seoul National University), Gunhee Kim (Seoul National University)
CodeFederated LearningMeta LearningImage
π― What it does: FedAvP is proposed in a federated learning environment, using shared augmentation strategies instead of shared data for local data augmentation.
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
Dario Fenoglio (UniversitΓ della Svizzera italiana), Marc Langheinrich (UniversitΓ della Svizzera italiana)
CodeFederated LearningSafty and PrivacyTabular
π― What it does: This paper proposes Federated Behavioural Planes (FBPs), which visualize and track the behavior of each client in federated learning through two behavioral planes (prediction error plane and counterfactual plane); based on FBPs, it designs Federated Behavioural Shields as a robust aggregation strategy to enhance the detection and defense against malicious clients.
π― What it does: BlackFed is proposed under the federated learning framework, achieving black-box distributed learning for semantic segmentation tasks without gradient or model information exchange.
π― What it does: A federated offline reinforcement learning algorithm named FEDORA is proposed and implemented, which enables multiple clients to collaboratively learn high-quality control policies without sharing data.
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Jiamu Bai (Pennsylvania State University), Yaliang Li (Alibaba Group)
CodeFederated LearningTransformerLarge Language ModelSupervised Fine-TuningText
π― What it does: This paper proposes FlexLoRA, an aggregation method for parameter-efficient fine-tuning of large language models in the context of federated learning, addressing the 'bucket effect' caused by the heterogeneity of resources and tasks across different clients.
Federated Graph Learning for Cross-Domain Recommendation
Ziqi Yang (Xiamen University), Xiaoliang Fan (Xiamen University)
CodeRecommendation SystemFederated LearningSafty and PrivacyGraph Neural NetworkGraph
π― What it does: A cross-domain recommendation framework FedGCDR based on federated graph learning is proposed, which supports multi-source domain knowledge transfer while preventing privacy leakage and negative transfer.
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
Bikang Pan (ShanghaiTech University), Ye Shi (ShanghaiTech University)
CodeFederated LearningTransformerPrompt EngineeringVision Language ModelImage
π― What it does: The paper proposes a federated learning framework based on the visual language foundation model CLIP, and achieves a combination of global and local prompts through prompt learning (PromptFolio) to balance the model's generalization ability and personalized adaptability.
π― What it does: A federated learning method called FLOCO is proposed, which utilizes pattern connectivity to construct a solution simplex and maps clients to sub-regions of the standard simplex through gradient similarity, allowing for local learning within these sub-regions while balancing global model and local personalization.
π― What it does: The FedMRL method is proposed, which utilizes a small homogeneous model shared by the server and heterogeneous models on the clients to achieve adaptive representation fusion and multi-level Matryoshka representation learning, addressing the issues of data, system, and model heterogeneity in model heterogeneous federated learning.
FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
Yuxin Yang (Jilin University), Binghui Wang (Illinois Institute of Technology)
CodeFederated LearningGraph Neural NetworkGraph
π― What it does: This paper proposes FedGMark, a provably robust backdoor watermarking method for federated graph learning (FedGL) models to protect model ownership.
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
Xiang Liu (National University of Singapore), Jialin Li (National University of Singapore)
CodeOptimizationFederated LearningImage
π― What it does: A one-round federated learning framework FedLPA is proposed, utilizing hierarchical posterior aggregation to achieve data-independent global model training.
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Zihan Tan (Wuhan University), Mang Ye (Wuhan University)
CodeFederated LearningGraph Neural NetworkGraph
π― What it does: Learning personalized graph neural networks for each client in a federated environment, and achieving cross-domain graph classification through spectral knowledge sharing and preference adjustment.
FEEL-SNN: Robust Spiking Neural Networks with Frequency Encoding and Evolutionary Leak Factor
Mengting Xu (Zhejiang University), Gang Pan (Zhejiang University)
CodeAdversarial AttackSpiking Neural NetworkImage
π― What it does: A robust spiking neural network named FEEL-SNN is designed and implemented, combining frequency encoding (FE) and evolutionary leakage factors (EL) to enhance resistance to adversarial and noise attacks.
π― What it does: This paper proposes the FERERO framework, modeling preference-guided multi-objective learning as a constrained vector optimization problem, and designs a single-loop primal algorithm and its stochastic variant, which can adaptively handle relative and absolute preferences, support controlled ascent, and avoid weak optimal solutions.
Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
Hanlin Gu (Webank), Lixin Fan (Webank)
CodeFederated LearningImageTextTabular
π― What it does: The Federated Feature Unlearning framework Ferrari is proposed, which achieves the unlearning of sensitive/backdoor/bias features in federated learning by minimizing feature sensitivity.
Few-Shot Adversarial Prompt Learning on Vision-Language Models
Yiwei Zhou (Beijing Institute of Technology), Tongliang Liu (University of Sydney)
CodeClassificationAdversarial AttackTransformerPrompt EngineeringVision Language ModelImageMultimodality
π― What it does: This paper proposes a few-shot adversarial prompt learning framework (FAP) that enhances the adversarial robustness of the pre-trained vision-language model CLIP by learning adjustable prompts and adversarial text supervision.
FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors
Shuai Liu (Sun Yat-sen University), Kai Huang (Sun Yat-sen University)
CodeObject DetectionAutonomous DrivingExplainability and InterpretabilityPoint Cloud
π― What it does: This paper proposes a feature factorization-based activation map (FFAM) method for generating visual explanations for LiDAR-based 3D detectors.
π― What it does: A model heterogeneous federated learning algorithm named FIARSE is designed and implemented, utilizing importance-aware sub-models to achieve client adaptive sub-model training and global model aggregation.
FIDE: Frequency-Inflated Conditional Diffusion Model for Extreme-Aware Time Series Generation
Asadullah Hill Galib (Michigan State University), Lifeng Luo (Michigan State University)
CodeGenerationData SynthesisAnomaly DetectionDiffusion modelTime SeriesSequentialBiomedical DataElectrocardiogramFinance Related
π― What it does: This paper proposes a temporal generation framework FIDE based on diffusion models, specifically designed to better preserve the distribution of extreme values (block extremes).
π― What it does: This paper proposes FIFO-Diffusion, a training-independent inference method that utilizes a pre-trained short video diffusion model to achieve infinite-length video generation through diagonal denoising and a queue mechanism. It also introduces latent space partitioning and lookahead denoising to enhance quality and stability.
Fight Back Against Jailbreaking via Prompt Adversarial Tuning
Yichuan Mo (Peking University), Yisen Wang (Peking University)
CodeSafty and PrivacyAdversarial AttackTransformerLarge Language ModelPrompt EngineeringText
π― What it does: This paper proposes Prompt Adversarial Tuning (PAT), which enhances the robustness of large language models against jailbreak attacks by inserting a trained defense prefix (control prompt) before user prompts during inference, while maintaining normal responses to legitimate requests.
FilterNet: Harnessing Frequency Filters for Time Series Forecasting
Kun Yi (North China Institute of Computing Technology), Wei Fan (University of Oxford)
CodeTransformerTime Series
π― What it does: A time series prediction framework called FilterNet based on learnable frequency filters is proposed, using instance normalization + FFT + learnable filtering + FFN for modeling.
π― What it does: A speech enhancement model named FINALLY is proposed, based on the HiFi++ architecture and incorporating WavLM perceptual loss and multi-stage training, capable of generating high-quality, studio-like clean speech at 48 kHz in a single forward inference.
FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
Yangyang Yu (Stevens Institute of Technology), Qianqian Xie
CodeOptimizationReinforcement Learning from Human FeedbackTransformerLarge Language ModelReinforcement LearningPrompt EngineeringTextTime SeriesFinance RelatedAudio
π― What it does: This paper presents FINCON, a multi-agent system based on large language models for single stock trading and portfolio management.
π― What it does: The NEMO method is proposed to locate memorized neurons in diffusion models and eliminate the model's replication of training samples by deactivating these neurons.
π― What it does: This paper proposes a technique called Edge Pruning, which uses gradient optimization to sparsify edges in Transformers, automatically discovering sparse computational subgraphs (Circuits) relevant to specific tasks.
π― What it does: By constructing the User Sequence Imagination (USIM) framework, the embedding of out-of-vocabulary (OOV) items without historical interactions is further optimized, thereby improving the recommendation effectiveness for OOV items.
π― What it does: The OLIVINE method is proposed, which generates weak semantic labels through a Visual Foundation Model (VFM) and conducts fine-grained image-point cloud contrastive learning on LiDAR point clouds to address self-conflict issues and enhance 3D representation learning.
π― What it does: The study investigates the behavior of models when fine-tuning a pre-trained classifier using only a subset of classes, finding that features are not forgotten. The main issue is the logit bias for known classes, which leads to a decrease in accuracy for missing classes. It proposes a post-processing calibration (adding a bias factor Ξ³) to restore the performance of missing classes.
π― What it does: This paper studies a fine-grained style personalization method for text-to-image models called FineStyle, which requires only a single style reference image.
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
Jay Shah (Colfax Research), Tri Dao (Princeton University)
CodeTransformer
π― What it does: A new Attention kernel FLASHATTENTION-3 has been implemented on the Hopper H100 GPU, utilizing asynchronous execution and low-precision FP8 to enhance the speed of attention computation in Transformers.
Qijian Zhang (City University of Hong Kong), Ying He (Nanyang Technological University)
CodePoint CloudMesh
π― What it does: A fully automatic unsupervised neural surface parameterization model FAM is proposed, capable of achieving global free boundary UV unfolding on arbitrary topologies, any mesh quality, and even unstructured point clouds, directly operating on discrete point sets without manual cutting.
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
Sukwon Yun (University of North Carolina), Tianlong Chen (University of North Carolina)
CodeClassificationRecognitionAnomaly DetectionTransformerMixture of ExpertsMultimodalityBiomedical DataAlzheimer's DiseaseElectronic Health Records
π― What it does: A multi-modal learning framework named Flex-MoE has been designed and implemented, capable of flexibly handling any combination of missing modalities;
Flexible task abstractions emerge in linear networks with fast and bounded units
Kai Jappe Sandbrink (Oxford Brain Mind Institute), Ali Hummos (Massachusetts Institute of Technology)
CodeOptimizationConvolutional Neural NetworkImage
π― What it does: This paper demonstrates that task abstraction (gate variables) can spontaneously form by jointly training linear networks with fast, non-negative, and finite activation gates using gradient descent, thereby achieving flexible task switching and combinatorial generalization.
π― What it does: This paper proposes FlexPlanner, a 3D layout planning method based on deep reinforcement learning, which can directly determine the 2D position, hierarchy, and aspect ratio of modules, and complete planning through multimodal representations (visual, graph, sequence).
π― What it does: We propose FlexSBDD, a flow-matching based deep generative model that can generate 3D ligand molecules binding to proteins while maintaining the structural flexibility of the proteins.
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
Ziyao Wang (University of Maryland), Ang Li (University of Maryland)
CodeFederated LearningSafty and PrivacyTransformerLarge Language ModelSupervised Fine-TuningText
π― What it does: FLORA is proposed, a noise-free global update method for heterogeneous low-rank adapter aggregation of large language models using LoRA within a federated learning framework.
π― What it does: An iterative algorithm (ICTM) is proposed that utilizes the prior of the flow matching model to perform MAP estimation on linear inverse problems (super-resolution, deblurring, inpainting, compressed sensing), achieving efficient recovery without the need for backpropagation through the ODE solver.
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
Anuroop Sriram (Meta), Brandon M Wood
CodeGenerationData SynthesisGraph Neural NetworkTransformerLarge Language ModelSupervised Fine-TuningFlow-based ModelText
π― What it does: This paper proposes a generative framework called FlowLLM that combines large language models (LLM) with Riemannian Flow Matching (RFM) for the efficient generation of stable and novel crystal materials.
FM-Delta: Lossless Compression for Storing Massive Fine-tuned Foundation Models
Wanyi Ning (Beijing University of Posts and Telecommunications), Ce Zhang (University of Chicago)
CodeCompressionSupervised Fine-Tuning
π― What it does: A lossless compression method FM-Delta is proposed for large-scale fine-tuning of foundational models in cloud storage, which compresses the complete fine-tuned model by mapping floating-point parameters to unsigned integers and performing range coding on the integer differences.
CodeTransformerLarge Language ModelTextMagnetic Resonance ImagingAudio
π― What it does: Using pre-trained language models of different scales (28 models, ranging from 124M to 14.2B parameters) to encode and model natural language auditory fMRI data, exploring the impact of model complexity on brain signal prediction.
FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
Kun Chen (Fudan University), LEI BAI
CodeTime Series
π― What it does: A method for assimilating observational data of arbitrary resolution based on Fourier Neural Processes (FNP) is proposed, which can directly fuse observations of different resolutions with the background to generate high-precision analysis fields.
π― What it does: This paper proposes FOOGD, a federated learning framework aimed at simultaneously addressing the issues of covariate shift (OOD normalization) and semantic shift (OOD detection) that arise in non-IID real-world data.
π― What it does: This paper proposes a new online continuous learning framework, NsCE, which addresses the issues of 'ignorance' and 'short-sightedness' that arise in single-channel learning.
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
Zhenyu Zhang (University of Texas at Austin), Zhangyang Wang (University of Texas at Austin)
CodeGenerationRetrievalTransformerLarge Language ModelText
π― What it does: Proposes a multi-scale position encoding (Ms-PoE) to address the 'intermediate loss' problem in LLMs with long contexts, achieving improved context utilization without additional training or overhead.
Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcasting
Chiu-Wai Yan (Hong Kong University of Science and Technology), Wai-Kin Wong (Hong Kong Observatory)
CodeGenerationData SynthesisConvolutional Neural NetworkRecurrent Neural NetworkTransformerImageTime Series
π― What it does: This paper proposes a frequency-domain-based loss functionβFourier Amplitude and Correlation Loss (FACL)βto replace the traditional pixel-level MSE, aiming to enhance the clarity and realism of precipitation nowcasting images.
Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation with Concept-Guided Feature Enhancement
Yanyan Huang (University of Hong Kong), Lequan Yu (University of Hong Kong)
CodeClassificationDomain AdaptationTransformerVision Language ModelContrastive LearningImageBiomedical Data
π― What it does: This paper proposes the CATE framework, which utilizes task-specific concept guidance in the pathology vision-language model to calibrate and enhance the features of general foundational models, thereby improving the performance of multi-instance learning (MIL) in whole slide image (WSI) classification tasks.
π― What it does: This paper proposes FreeSplat, a general-purpose 3D Gaussian scattering method that can reconstruct global 3D Gaussians from indoor perspective sequences of arbitrary length and achieve free-viewpoint rendering.
Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
Weiwei Ye (Central South University), Ning Gui (Central South University)
CodeTime Series
π― What it does: This paper proposes a new instance normalization method called Frequency Adaptive Normalization (FAN) to address trend and seasonality issues in non-stationary time series.
π― What it does: This paper proposes a video-to-audio generation model called FRIEREN based on rectified flow matching, which can directly generate high-quality audio synchronized with visuals from silent videos.
π― What it does: This study proposes a method to learn the characteristic functions and eigenvalues of dynamical systems from biased simulation data using an unbiased infinitesimal generator (through its inverse operator).
π― What it does: This paper proposes a task-specific adapter generation through instruction learning (TAGI), enabling the construction of task-specific models without instance training, simulating how humans learn skills through understanding instructions.
From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection
Xinlei Wang (University of Sydney), Junhua Zhao (Chinese University of Hong Kong)
CodeTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextTime SeriesFinance RelatedChain-of-Thought
π― What it does: A framework is proposed that combines large language models with reasoning agents to predict news events by integrating them with time series data;
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Jialin Chen (Yale University), Rex Ying (Yale University)
CodeTime SeriesFinance Related
π― What it does: Designed and implemented the Channel Clustering Module (CCM), which manages channel clustering for multi-channel time series prediction by clustering channels and learning prototypes.
π― What it does: This study investigates the use of BadNets-style data poisoning attacks in diffusion models, revealing the bidirectional poisoning effects (trigger amplification and phase shift) and their impact on generation quality and classification robustness.
π― What it does: This paper proposes using frozen visual foundation models (such as CLIP) as feature enhancers to improve query-based object detectors (DETR series) by using class tokens as image queries and patch tokens for encoder feature fusion, thereby enhancing detection performance.
Frustratingly Easy Test-Time Adaptation of Vision-Language Models
Matteo Farina (University of Trento), Elisa Ricci (Fondazione Bruno Kessler)
CodeClassificationDomain AdaptationTransformerVision Language ModelImageMultimodality
π― What it does: This paper proposes a zero-temperature testing adaptation method called ZERO, which achieves instant enhancement of visual-language models through single forward propagation.
π― What it does: A Feature-Universal Graph (FUG) contrastive pre-training strategy is proposed, enabling graph neural networks to directly transfer on graph data with different node feature shapes without the need to rebuild models or preprocess features.
Aditya Bommakanti (Indian Institute of Technology Delhi), Panagiotis Karras (University of Copenhagen)
CodeOptimizationGraph Neural NetworkGraph
π― What it does: This paper proposes FUGAL, a mediator-free graph alignment method that operates directly on the adjacency matrix. It relaxes the permutation matrix into a doubly stochastic matrix, uses Frank-Wolfe and Sinkhorn-Knopp iterations for solving, incorporates a linear alignment regularization based on structural features into the objective, and finally obtains the final matching through the Hungarian algorithm.
Full-Distance Evasion of Pedestrian Detectors in the Physical World
Zhi Cheng (Tsinghua University), Xiaolin Hu (Tsinghua University)
CodeObject DetectionAdversarial AttackConvolutional Neural NetworkImagePhysics Related
π― What it does: A full-distance attack (FDA) method is proposed to generate effective adversarial patterns against pedestrian detectors in the physical world.
Fully Distributed, Flexible Compositional Visual Representations via Soft Tensor Products
Bethia Sun (University of New South Wales Sydney), Yang Song (University of New South Wales Sydney)
CodeRepresentation LearningAuto EncoderImage
π― What it does: This paper proposes a fully distributed and flexible Soft TPR (Soft Tensor Product Representation) and the corresponding Soft TPR Autoencoder for learning distributed representations of decomposable components in visual data.
π― What it does: A new paradigm for bi-level optimization is proposed in function space rather than parameter space, along with the corresponding function implicit differentiation theory and an implementable algorithm (FuncID).
π― What it does: This paper studies convex bilevel optimization problems, proving that any zero-respect first-order method cannot achieve an absolute optimal solution, and proposes a new algorithm called FC-BiO, which can achieve near-optimal iteration complexity under weak optimality error.
Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
Alliot Nagle (University of Texas at Austin), Hyeji Kim (University of Texas at Austin)
CodeCompressionTransformerLarge Language ModelPrompt EngineeringText
π― What it does: A theoretical framework for prompt compression of black-box large language models is proposed, deriving its rate-distortion function and experimentally validating the gap with theoretical limits; at the same time, an Adaptive QuerySelect algorithm that is query-aware and variable-rate is introduced.
FuseAnyPart: Diffusion-Driven Facial Parts Swapping via Multiple Reference Images
Zheng Yu (Shanghai Jiao Tong University), Senzhang Wang (Central South University)
CodeGenerationData SynthesisDiffusion modelImage
π― What it does: FuseAnyPart is a facial component swapping method based on diffusion models, capable of seamlessly blending components such as eyes, nose, and mouth from multiple source images onto a target face to generate high-quality, natural synthetic faces.
π― What it does: A one-round communication federated learning method called FuseFL is proposed, which reduces communication costs and enhances model generalization ability by layer-wise fusion of intermediate features from client models.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Xiaoxin He (National University of Singapore), Bryan Hooi (National University of Singapore)
CodeGenerationRetrievalExplainability and InterpretabilityComputational EfficiencyGraph Neural NetworkLarge Language ModelPrompt EngineeringTextGraphBenchmarkRetrieval-Augmented Generation
π― What it does: A question-answering benchmark for real text graphs has been constructed, and the G-Retriever framework has been proposed to support dialogue with graphs.
G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training
Che Liu (Imperial College London), Rossella Arcucci (Imperial College London)
CodeObject DetectionSegmentationRepresentation LearningConvolutional Neural NetworkVision Language ModelContrastive LearningImageBiomedical Data
π― What it does: G2D proposes a medical vision-language pre-training framework that can learn both global and pixel-level visual representations simultaneously.
G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models
Pengyue Jia (City University of Hong Kong), Dawei Yin (Baidu Inc.)
CodeRetrievalTransformerVision Language ModelImageTextMultimodalityRetrieval-Augmented Generation
π― What it does: This paper proposes a global image geolocation framework named G3, which utilizes a retrieval-enhanced generation method to achieve more accurate coordinate predictions.
Huiping Zhuang (South China University of Technology), Cen Chen (Shenzhen Institute, Hunan University)
CodeClassificationRecognitionTransformerImage
π― What it does: A novel example-free generalized analysis continuous learning method (GACL) is proposed, capable of completing the GCIL task without storing historical samples.
π― What it does: A Gated Slot Attention (GSA) mechanism is proposed, incorporating gating based on ABC to balance memory forgetting and recent bias, improving the performance of linear attention models on long sequences and memory-intensive tasks while maintaining a small state size.
GAVEL: Generating Games via Evolution and Language Models
Graham Todd (New York University), Julian Togelius (New York University)
CodeGenerationTransformerLarge Language ModelSupervised Fine-TuningText
π― What it does: Automatically generate novel playable games using large language models and evolutionary algorithms on the Ludii game description language.
GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
Guibin Zhang (Tongji University), Kun Wang (NTU)
CodeGraph Neural NetworkGraph
π― What it does: A soft data pruning method GDeR based on prototype learning is proposed, which dynamically maintains the training subset to improve the training efficiency, balance, and robustness of graph neural networks.
Raphael Baena (Ecole des Ponts), Mathieu Aubry (Ecole des Ponts)
CodeRecognitionTransformerText
π― What it does: This paper proposes a detection-based text line recognition method (DTLR), which achieves universal line text recognition for different writing systems (Latin letters, Chinese characters, and ciphers) through large-scale synthetic data pre-training and line-level fine-tuning on a Transformer detector.
π― What it does: A real-time replayable full-body 3D Gaussian model based on a single image is proposed, achieving unidirectional forward head reconstruction and expression control.
π― What it does: This study addresses and resolves the bias effect caused by data augmentation in domain generalization for person re-identification, proposing the BAU framework that maintains alignment and uniformity in the representation space.
Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming
Shinsaku Sakaue (University of Tokyo), Taihei Oki (Hokkaido University)
CodeOptimizationTabular
π― What it does: This paper studies a data-driven projection method that uses a trained projection matrix to reduce the dimensionality of high-dimensional linear programming for solving, and then maps the solution back to the original space, improving the efficiency and quality of the solution.
π― What it does: By jointly generating data with semantic shift and domain shift, and employing a two-stage noise adaptive training, the detection and generalization capabilities of semantic segmentation under multiple distribution shifts are enhanced.
Generalized Linear Bandits with Limited Adaptivity
Ayush Sawarni (Stanford University), Gaurav Sinha (Microsoft Research India)
CodeTabular
π― What it does: Under finite adaptive constraints, the bandit problem in a generalized linear context is studied, and two algorithms, B-GLinCB (M1 setting) and RS-GLinCB (M2 setting), are proposed;