Deep Learning
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.
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/
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets
Devansh Arpit, Vรญctor Campos, Yoshua Bengio
Neural Information Processing Systems http://nips.cc/
Improving Diffusion-Based Image Synthesis with Context Prediction Ling Y ang
Diffusion models are a new class of generative models, and have dramatically promoted image generation with unprecedented quality and diversity.
Predicting the Politics of an Image Using Webly Supervised Data
Christopher Thomas, Adriana Kovashka
Neural Information Processing Systems http://nips.cc/
