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Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li
Graph embedding has been intensively studied recently, due to the advance of various neural networkmodels.
Gradientbasedsampleselectionforonlinecontinual learning
Acontinual learning agent learns online with anon-stationary andnever-ending stream ofdata. Thekeytosuch learning process istoovercome thecatastrophic forgetting of previously seen data, which is a well known problem of neuralnetworks.
Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models
Yining Wang, Xi Chen, Yuan Zhou
In this paper we consider the dynamic assortment selection problem under an uncapacitated multinomial-logit (MNL) model. By carefully analyzing a revenue potential function, we show that a trisection based algorithm achieves an item-independent regret bound of Op? T log log T q, which matches information theoretical lower bounds up to iterated logarithmic terms. Our proof technique draws tools from the unimodal/convex bandit literature as well as adaptive confidence parameters in minimax multi-armed bandit problems.
Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning Sriyash Poddar, Y anming Wan
While conceptually simple, we show that in practice, this reward modeling requires careful algorithmic considerations around model architecture and reward scaling.
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation
Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama
Neural Information Processing Systems http://nips.cc/
Adversarial Robustness through Random Weight Sampling
Deep neural networks have been found to be vulnerable in a variety of tasks. Adversarial attacks can manipulate network outputs, resulting in incorrect predictions.
How to Start Training: The Effect of Initialization and Architecture
Neural Information Processing Systems http://nips.cc/
MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention Huiqiang Jiang, Yucheng Li
Existing methods for speeding up pre-filling often fail to maintain acceptable accuracy or efficiency when applied to long-context LLMs.
Deep Generative Models with Learnable Knowledge Constraints
Zhiting Hu, Zichao Yang, Russ R. Salakhutdinov, LIANHUI Qin, Xiaodan Liang, Haoye Dong, Eric P. Xing
Neural Information Processing Systems http://nips.cc/