default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDTransactions on Machine Learning Research, Volume 2025
Volume 2025, 2025
- Benjamin Cohen-Wang, Joshua Vendrow, Aleksander Madry:
Ask Your Distribution Shift if Pre-Training is Right for You. - Shubhankar Gupta, Saksham Sharma, Suresh Sundaram:
Reward-based Autonomous Online Learning Framework for Resilient Cooperative Target Monitoring using a Swarm of Robots. - Wenhao Lu, Xufeng Zhao, Josua Spisak, Jae Hee Lee, Stefan Wermter:
Mental Modelling of Reinforcement Learning Agents by Language Models. - Debarshi Brahma, Anuska Roy, Soma Biswas:
Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts. - Myeongho Jeon, Suhwan Choi, Hyoje Lee, Teresa Yeo:
An Analysis of Model Robustness across Concurrent Distribution Shifts. - Madison Cooley, Varun Shankar, Mike Kirby, Shandian Zhe:
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases. - Weijian Luo:
Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences. - David Chiang:
Transformers in Uniform TC0. - Steven Jecmen, Nihar B. Shah, Fei Fang, Leman Akoglu:
On the Detection of Reviewer-Author Collusion Rings From Paper Bidding. - Yuan Zang, Tian Yun, Hao Tan, Trung Bui, Chen Sun:
Pre-trained Vision-Language Models Learn Discoverable Visual Concepts. - Peihong Yu, Manav Mishra, Alec Koppel, Carl E. Busart, Priya Narayan, Dinesh Manocha, Amrit Singh Bedi, Pratap Tokekar:
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning. - Tim Z. Xiao, Johannes Zenn, Robert Bamler:
A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data and Overparameterization? - Dominik Fay, Sebastian Mair, Jens Sjölund:
Personalized Privacy Amplification via Importance Sampling. - Alexander Larionov, Niall M. Adams, Kevin N. Webster:
Investigating the impact of missing value handling on Boosted trees and Deep learning for Tabular data: A Claim Reserving case study. - Franka Bause, Fabian Jogl, Patrick Indri, Tamara Drucks, David Penz, Nils Morten Kriege, Thomas Gärtner, Pascal Welke, Maximilian Thiessen:
Maximally Expressive GNNs for Outerplanar Graphs. - Shruti P. Maralappanavar, Prashant Khanduri, B. N. Bharath:
Linear Convergence of Decentralized FedAvg for PL Objectives: The Interpolation Regime. - Yihang Gao, Chuanyang Zheng, Enze Xie, Han Shi, Tianyang Hu, Yu Li, Michael Ng, Zhenguo Li, Zhaoqiang Liu:
AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures. - Yulei Qin, Yuncheng Yang, Pengcheng Guo, Gang Li, Hang Shao, Yuchen Shi, Zihan Xu, Yun Gu, Ke Li, Xing Sun:
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models. - Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Joshua M. Susskind, Navdeep Jaitly, Shuangfei Zhai:
Improving GFlowNets for Text-to-Image Diffusion Alignment. - Sahil Verma, Gantavya Bhatt, Avi Schwarzschild, Soumye Singhal, Arnav Mohanty Das, Chirag Shah, John P. Dickerson, Pin-Yu Chen, Jeff Bilmes:
Effective Backdoor Mitigation in Vision-Language Models Depends on the Pre-training Objective. - Manu Gaur, Darshan Singh S, Makarand Tapaswi:
No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image Captioning. - Miles Everett, Mingjun Zhong, Georgios Leontidis:
Masked Capsule Autoencoders. - Suryam Arnav Kalra, Arindam Biswas, Pabitra Mitra, Biswajit Basu:
Sparse Neural Architectures via Deterministic Ramanujan Graphs. - Chloe Loughridge, Qinyi Sun, Seth Ahrenbach, Federico Cassano, Chuyue Sun, Ying Sheng, Anish Mudide, Md Rakib Hossain Misu, Nada Amin, Max Tegmark:
DafnyBench: A Benchmark for Formal Software Verification. - Clément Bonet, Kimia Nadjahi, Thibault Séjourné, Kilian Fatras, Nicolas Courty:
Slicing Unbalanced Optimal Transport. - Amitangshu Mukherjee, Timur Ibrayev, Kaushik Roy:
On Inherent Adversarial Robustness of Active Vision Systems. - Marco Paul E. Apolinario, Kaushik Roy:
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks. - Tobias Leemann, Alina Fastowski, Felix Pfeiffer, Gjergji Kasneci:
Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers. - Yifei He, Yuzheng Hu, Yong Lin, Tong Zhang, Han Zhao:
Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic. - Peter Matthew Jacobs, Lekha Patel, Anirban Bhattacharya, Debdeep Pati:
Minimax Posterior Contraction Rates for Unconstrained Distribution Estimation on [0, 1]d under Wasserstein Distance. - Kangfu Mei, Zhengzhong Tu, Mauricio Delbracio, Hossein Talebi, Vishal M. Patel, Peyman Milanfar:
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models. - Bingxin Zhou, Outongyi Lv, Jing Wang, Xiang Xiao, Weishu Zhao:
ODNet: Opinion Dynamics-Inspired Neural Message Passing for Graphs and Hypergraphs. - Seth Neel:
PRIMO: Private Regression in Multiple Outcomes. - Tobias Fuchs, Florian Kalinke, Klemens Böhm:
Partial-Label Learning with a Reject Option. - Stefano Peluchetti:
BM2: Coupled Schrödinger Bridge Matching. - Vidhi Lalchand, Anna-Christina Eilers:
Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative model for Quasar spectra. - Pedro Cisneros-Velarde, Zhijie Chen, Sanmi Koyejo, Arindam Banerjee:
Optimization and Generalization Guarantees for Weight Normalization. - Eduardo Fernandes Montesuma, Fred Maurice Ngolè Mboula, Antoine Souloumiac:
Optimal Transport for Domain Adaptation through Gaussian Mixture Models. - Zidu Yin, Zhen Zhang, Dong Gong, Stefano V. Albrecht, Javen Qinfeng Shi:
Highway Graph to Accelerate Reinforcement Learning. - Saeideh Ghanbari Azar, Lorenzo Tronchin, Attila Simkó, Tufve Nyholm, Tommy Löfstedt:
From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning. - Arman Rahbar, Niklas Åkerblom, Morteza Haghir Chehreghani:
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach. - Yikai Zhang, Jiahe Lin, Fengpei Li, Songzhu Zheng, Anant Raj, Anderson Schneider, Yuriy Nevmyvaka:
Reweighting Improves Conditional Risk Bounds. - Lei Zhao, Lin Cai, Wu-Sheng Lu:
Federated Learning with Efficient Local Adaptation for Realized Volatility Prediction. - Dominik Baumann, Erfaun Noorani, James Price, Ole Peters, Colm Connaughton, Thomas B. Schön:
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations. - Marc T. Law, Karsten Kreis, Haggai Maron:
Directed Graph Generation with Heat Kernels. - Shuai Zhao, Meihuizi Jia, Zhongliang Guo, Leilei Gan, Xiaoyu Xu, Xiaobao Wu, Jie Fu, Yichao Feng, Fengjun Pan, Anh Tuan Luu:
A Survey of Recent Backdoor Attacks and Defenses in Large Language Models. - Nimrod Berman, Eitan Kosman, Dotan Di Castro, Omri Azencot:
Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes. - Nayoung Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park:
Decoupled Sequence and Structure Generation for Realistic Antibody Design. - Nicolas Boizard, Kevin El Haddad, Céline Hudelot, Pierre Colombo:
Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs. - Adarsh Kappiyath, Anmol Garg, Ramya Hebbalaguppe, Prathosh AP:
Lifelong Learning in StyleGAN through Latent Subspaces. - Leah Bar, Boaz Lerner, Nir Darshan, Rami Ben-Ari:
Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval. - Alejandro Guerra-Manzanares, Farah Shamout:
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks. - Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu:
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior. - Guiliang Liu, Sheng Xu, Shicheng Liu, Ashish Gaurav, Sriram Ganapathi Subramanian, Pascal Poupart:
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges. - Subba Reddy Oota, Zijiao Chen, Manish Gupta, Bapi Raju Surampudi, Gaël Jobard, Frédéric Alexandre, Xavier Hinaut:
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey). - Eugene A. Golikov:
A Generalization Bound for Nearly-Linear Networks. - Weicheng Zhu, Sheng Liu, Carlos Fernandez-Granda, Narges Razavian:
Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling. - Hiroyuki Sakai, Hideaki Iiduka:
A general framework of Riemannian adaptive optimization methods with a convergence analysis. - Tal Reiss, Yedid Hoshen:
An Attribute-based Method for Video Anomaly Detection. - Chun-Yin Huang, Ruinan Jin, Can Zhao, Daguang Xu, Xiaoxiao Li:
Federated Learning on Virtual Heterogeneous Data with Local-Global Dataset Distillation. - Oskar Nordenfors, Fredrik Ohlsson, Axel Flinth:
Optimization Dynamics of Equivariant and Augmented Neural Networks. - Paul Brunzema, Alexander von Rohr, Friedrich Solowjow, Sebastian Trimpe:
Event-Triggered Time-Varying Bayesian Optimization. - Thomas Pethick, Parameswaran Raman, Lenon Minorics, Mingyi Hong, Shoham Sabach, Volkan Cevher:
νSAM: Memory-Efficient Sharpness-Aware Minimization via Nuclear Norm Constraints. - Netta Ollikka, Amro Abbas, Andrea Perin, Markku Kilpeläinen, Stéphane Deny:
A comparison between humans and AI at recognizing objects in unusual poses. - David Mueller, Mark Dredze, Nicholas Andrews:
Can Optimization Trajectories Explain Multi-Task Transfer? - Cen-You Li, Olaf Dünnbier, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer:
Global Safe Sequential Learning via Efficient Knowledge Transfer. - Stanislas Strasman, Antonio Ocello, Claire Boyer, Sylvain Le Corff, Vincent Lemaire:
An analysis of the noise schedule for score-based generative models. - Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx:
On the Properties and Estimation of Pointwise Mutual Information Profiles. - Luciana Ferrer, Daniel Ramos:
Evaluating Posterior Probabilities: Decision Theory, Proper Scoring Rules, and Calibration. - Koki Okajima, Tomoyuki Obuchi:
Transfer Learning in ℓ1 Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis. - Ruisu Zhang, Yicong Chen, Kangwook Lee:
Improving CLIP Counting Accuracy via Parameter-Efficient Fine-Tuning. - Chuanhui Liu, Xiao Wang:
Doubly Robust Conditional VAE via Decoder Calibration: An Implicit KL Annealing Approach. - Luca Simi:
A Scalable Approach for Mapper via Efficient Spatial Search. - Alexey Kravets, Vinay P. Namboodiri:
Zero-shot CLIP Class Forgetting via Text-image Space Adaptation. - Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, Yongdai Kim:
Fairness Through Matching. - Sai Saketh Rambhatla, Ishan Misra:
SelfEval: Leveraging discriminative nature of generative models for evaluation. - Zhiyu Guo, Hidetaka Kamigaito, Taro Watanabe:
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models. - Prithviraj Tarale, Edward A. Rietman, Hava T. Siegelmann:
Distributed Multi-Agent Lifelong Learning. - Yu Wang, Chi Han, Tongtong Wu, Xiaoxin He, Wangchunshu Zhou, Nafis Sadeq, Xiusi Chen, Zexue He, Wei Wang, Gholamreza Haffari, Heng Ji, Julian J. McAuley:
Towards LifeSpan Cognitive Systems. - Zhuoran Yu, Chenchen Zhu, Sean Culatana, Raghuraman Krishnamoorthi, Fanyi Xiao, Yong Jae Lee:
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images. - Tanguy Bosser, Souhaib Ben Taieb:
Preventing Conflicting Gradients in Neural Marked Temporal Point Processes. - Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi:
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE. - Chao-Kai Chiang, Masashi Sugiyama:
Unified Risk Analysis for Weakly Supervised Learning. - Dun Zeng, Zenglin Xu, Yu Pan, Xu Luo, Qifan Wang, Xiaoying Tang:
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance. - Riccardo Majellaro, Jonathan Collu, Aske Plaat, Thomas M. Moerland:
Explicitly Disentangled Representations in Object-Centric Learning. - Nicholas Krämer:
Numerically Robust Fixed-Point Smoothing Without State Augmentation. - Joseph Paul Cohen, Louis Blankemeier, Akshay S. Chaudhari:
Identifying Spurious Correlations using Counterfactual Alignment. - Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian:
Semantic Alignment for Prompt-Tuning in Vision Language Models. - Georgios Vlassis, David Belius, Volodymyr Fomichov:
A thorough reproduction and evaluation of µP. - Zhuo Zhi, Yuxuan Sun, Qiangqiang Wu, Ziquan Liu, Miguel R. D. Rodrigues:
Wasserstein Modality Alignment Makes Your Multimodal Transformer More Robust. - Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi, Alain Rakotomamonjy, Mike Gartrell, Jamal Atif, Alexandre Allauzen:
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance. - Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, Soheil Feizi:
Can AI-Generated Text be Reliably Detected? Stress Testing AI Text Detectors Under Various Attacks. - Nauman Ahad, Mark A. Davenport, Eva L. Dyer:
Time Series Domain Adaptation via Channel-Selective Representation Alignment. - Naveen Karunanayake, Suranga Seneviratne, Sanjay Chawla:
ExCeL: Combined Extreme and Collective Logit Information for Out-of-Distribution Detection. - Meher Chaitanya, Kshitijaa Jaglan, Ulrik Brandes:
Adjacency Search Embeddings. - Michal Derezinski:
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches. - Bolian Li, Ruqi Zhang:
Making Reliable and Flexible Decisions in Long-tailed Classification. - Georgios Sidiropoulos, Samarth Bhargav, Panagiotis Eustratiadis, Evangelos Kanoulas:
Multivariate Dense Retrieval: A Reproducibility Study under a Memory-limited Setup. - Shayan Mohajer Hamidi, Linfeng Ye:
Distributed Quasi-Newton Method for Fair and Fast Federated Learning. - Spandan Madan, Tomotake Sasaki, Hanspeter Pfister, Tzu-Mao Li, Xavier Boix:
In-distribution adversarial attacks on object recognition models using gradient-free search. - Hongyi Ling, Zhimeng Jiang, Na Zou, Shuiwang Ji:
Counterfactual Fairness on Graphs: Augmentations, Hidden Confounders, and Identifiability. - Anastasis Kratsios, Haitz Sáez de Ocáriz Borde, Takashi Furuya, Marc T. Law:
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts. - Zhepeng Cen, Yao Liu, Siliang Zeng, Pratik Chaudhari, Huzefa Rangwala, George Karypis, Rasool Fakoor:
Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens. - Jieru Mei, Liang-Chieh Chen, Alan L. Yuille, Cihang Xie:
SPFormer: Enhancing Vision Transformer with Superpixel Representation. - Yuzhu Mao, Zihao Zhao, Siqi Ping, Yang Liu, Wenbo Ding:
Enhancing Parameter Efficiency and Generalization in Large Models: A Regularized and Masked Low-Rank Adaptation Approach. - Carlos Mougan, Klaus Broelemann, Gjergji Kasneci, Thanassis Tiropanis, Steffen Staab:
Explanation Shift: How Did the Distribution Shift Impact the Model? - Masih Eskandar, Tooba Imtiaz, Zifeng Wang, Jennifer G. Dy:
ADAPT to Robustify Prompt Tuning Vision Transformers. - Xinyu Tang, Ashwinee Panda, Milad Nasr, Saeed Mahloujifar, Prateek Mittal:
Private Fine-tuning of Large Language Models with Zeroth-order Optimization. - Chao Zhou, Huishuai Zhang, Jiang Bian, Weiming Zhang, Nenghai Yu:
\copyright Plug-in Authorization for Human Copyright Protection in Text-to-Image Model. - Boyi Li, Philipp Wu, Pieter Abbeel, Jitendra Malik:
Interactive Task Planning with Language Models. - Edvin Listo Zec, Tom Hagander, Eric Ihre-Thomason, Sarunas Girdzijauskas:
On the effects of similarity metrics in decentralized deep learning under distribution shift. - Gabriel Dubé, Mario Marchand:
Shapley Values of Structured Additive Regression Models and Application to RKHS Weightings of Functions. - Florian Kalinke, Marco Heyden, Georg Gntuni, Edouard Fouché, Klemens Böhm:
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection. - Michele Miranda, Elena Sofia Ruzzetti, Andrea Santilli, Fabio Massimo Zanzotto, Sébastien Bratières, Emanuele Rodolà:
Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions. - Muhammed Fatih Balin, Dominique LaSalle, Ümit V. Çatalyürek:
Cooperative Minibatching in Graph Neural Networks. - Nicholas Bai, Rahul A. Iyer, Tuomas P. Oikarinen, Akshay R. Kulkarni, Tsui-Wei Weng:
Interpreting Neurons in Deep Vision Networks with Language Models. - Noureddine Henka, Mohamad Assaad, Sami Tazi:
Mixture Degree-Corrected Stochastic Block Model for Multi-Group Community Detection in Multiplex Graphs. - Sebastian Wankerl, Jan Pfister, Andrzej Dulny, Gerhard Götz, Andreas Hotho:
Identifying Axiomatic Mathematical Transformation Steps using Tree-Structured Pointer Networks. - Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov, Samuel Horváth:
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity. - Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi:
Bridging Causality, Individual Fairness, and Adversarial Robustness in the Absence of Structural Causal Model. - Michele Caprio, David Stutz, Shuo Li, Arnaud Doucet:
Conformalized Credal Regions for Classification with Ambiguous Ground Truth. - Geri Skenderi, Hang Li, Jiliang Tang, Marco Cristani:
Graph-level Representation Learning with Joint-Embedding Predictive Architectures. - Zidan Wang, Rui Shen, Bradly C. Stadie:
Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs. - Bas van der Heijden, Jens Kober, Robert Babuska, Laura Ferranti:
REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation. - Travis E. Gibson, Sawal Acharya, Anjali Parashar, Joseph E. Gaudio, Anuradha Annaswamy:
On the stability of gradient descent with second order dynamics for time-varying cost functions. - Motasem Alfarra, Alvaro H. C. Correia, Bernard Ghanem, Christos Louizos:
Test-Time Adaptation with Source Based Auxiliary Tasks. - Saptarshi Chakraborty:
Minimax Lower Bounds for Estimating Distributions on Low-dimensional Spaces. - Saleh Gholam Zadeh, Vaisakh Shaj, Patrick Jahnke, Gerhard Neumann, Tim Breitenbach:
Towards Measuring Predictability: To which extent data-driven approaches can extract deterministic relations from data exemplified with time series prediction and classification. - Théo Vincent, Daniel Palenicek, Boris Belousov, Jan Peters, Carlo D'Eramo:
Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning. - Chandramouli Shama Sastry, Mahdi Gilany, Kry Yik-Chau Lui, Martin Magill, Alexander Pashevich:
DeepRRTime: Robust Time-series Forecasting with a Regularized INR Basis. - Jackson Petty, Sjoerd van Steenkiste, Tal Linzen:
How Does Code Pretraining Affect Language Model Task Performance? - Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Israfil Bahceci, Akram Bin Sediq, Hamza Umit Sokun, Nicolas Papernot:
Selective Prediction via Training Dynamics. - Arash Behboodi, Gabriele Cesa:
On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing. - Minguk Jang, Hye Won Chung:
Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation. - Hikari Otsuka, Daiki Chijiwa, Ángel López García-Arias, Yasuyuki Okoshi, Kazushi Kawamura, Thiem Van Chu, Daichi Fujiki, Susumu Takeuchi, Masato Motomura:
Partially Frozen Random Networks Contain Compact Strong Lottery Tickets. - Jiazheng Li, Jundong Li, Chuxu Zhang:
Instance-Aware Graph Prompt Learning. - Savvas Melidonis, Yiming Xi, Konstantinos C. Zygalakis, Yoann Altmann, Marcelo Pereyra:
Score-Based Denoising Diffusion Models for Photon-Starved Image Restoration Problems. - Haonan Wang, Qian Liu, Chao Du, Tongyao Zhu, Cunxiao Du, Kenji Kawaguchi, Tianyu Pang:
When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training. - Pengyun Wang, Yadi Cao, Chris Russell, Yanxin Shen, Junyu Luo, Ming Zhang, Siyu Heng, Xiao Luo:
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation. - Ibrahim Serouis, Florence Sèdes:
Towards context and domain-aware algorithms for scene analysis. - Luca Butera, Giovanni de Felice, Andrea Cini, Cesare Alippi:
On the Regularization of Learnable Embeddings for Time Series Forecasting. - Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li:
LLaVA-OneVision: Easy Visual Task Transfer. - Zhi Chen, Yufan Ren, Tong Zhang, Zheng Dang, Wenbing Tao, Sabine Süsstrunk, Mathieu Salzmann:
Adaptive Multi-step Refinement Network for Robust Point Cloud Registration. - Liran Nochumsohn, Omri Azencot:
Data Augmentation Policy Search for Long-Term Forecasting. - Ya Song, Laurens Bliek, Yaoxin Wu, Yingqian Zhang:
Enhancing Remaining Useful Life Prediction with Ensemble Multi-Term Fourier Graph Neural Networks. - Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag:
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. - Anna Hedström, Philine Lou Bommer, Thomas F. Burns, Sebastian Lapuschkin, Wojciech Samek, Marina M.-C. Höhne:
Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions. - Giovanni Luca Marchetti, Gabriele Cesa, Kumar Pratik, Arash Behboodi:
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach. - Neil Ashtekar, Jingxi Zhu, Vasant G. Honavar:
Class Incremental Learning from First Principles: A Review. - Aymene Mohammed Bouayed, Samuel Deslauriers-Gauthier, Adrian Iacovelli, David Naccache:
CNN Interpretability with Multivector Tucker Saliency Maps for Self-Supervised Models.
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-06-19 01:41 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
