Papers
Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models
Mingjia Huo, Sai Ashish Somayajula, Youwei Liang et al.
Topological Neural Networks go Persistent, Equivariant, and Continuous
Yogesh Verma, Amauri H Souza, Vikas Garg
Total Variation Distance Meets Probabilistic Inference
Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel et al.
Total Variation Floodgate for Variable Importance Inference in Classification
Wenshuo Wang, Lucas Janson, Lihua Lei et al.
To the Max: Reinventing Reward in Reinforcement Learning
Grigorii Veviurko, Wendelin Boehmer, Mathijs De Weerdt
Toward Adaptive Reasoning in Large Language Models with Thought Rollback
Sijia Chen, Baochun Li
Toward Availability Attacks in 3D Point Clouds
Yifan Zhu, Yibo Miao, Yinpeng Dong et al.
Towards a Better Theoretical Understanding of Independent Subnetwork Training
Egor Shulgin, Peter Richtárik
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model
Mikail Khona, Maya Okawa, Jan Hula et al.
Towards a Self-contained Data-driven Global Weather Forecasting Framework
Yi Xiao, Lei Bai, Wei Xue et al.
Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components
Zhiliang Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil et al.
Towards Certified Unlearning for Deep Neural Networks
Binchi Zhang, Yushun Dong, Tianhao Wang et al.
Towards Compositionality in Concept Learning
Adam Stein, Aaditya Naik, Yinjun Wu et al.
Towards efficient deep spiking neural networks construction with spiking activity based pruning
Yaxin Li, Qi Xu, Jiangrong Shen et al.
Towards Efficient Exact Optimization of Language Model Alignment
Haozhe Ji, Cheng Lu, Yilin Niu et al.
Towards Efficient Spiking Transformer: a Token Sparsification Framework for Training and Inference Acceleration
Zhengyang Zhuge, Peisong Wang, Xingting Yao et al.
Towards Efficient Training and Evaluation of Robust Models against $l_0$ Bounded Adversarial Perturbations
Xuyang Zhong, Yixiao Huang, Chen Liu
Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph
Yufei Kuang, Jie Wang, Yuyan Zhou et al.
Towards Generalization beyond Pointwise Learning: A Unified Information-theoretic Perspective
Yuxin Dong, Tieliang Gong, Hong Chen et al.
Towards General Neural Surrogate Solvers with Specialized Neural Accelerators
Chenkai Mao, Robert Lupoiu, Tianxiang Dai et al.
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
Bhrij Patel, Wesley A Suttle, Alec Koppel et al.
Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
Yibo Yang, Xiaojie Li, Motasem Alfarra et al.
Towards Modular LLMs by Building and Reusing a Library of LoRAs
Oleksiy Ostapenko, Zhan Su, Edoardo Ponti et al.
Towards Neural Architecture Search through Hierarchical Generative Modeling
Lichuan Xiang, Łukasz Dudziak, Mohamed S Abdelfattah et al.