Deep Learning
Robust Sleep Staging over Incomplete Multimodal Physiological Signals via Contrastive Imagination Qi Shen
Furthermore, the exploration of temporal context information within PSs is also a serious challenge.
Meta-Learning Universal Priors Using Non-Injective Change of Variables
Critical in this context is the prior knowledge accumulated from related tasks.
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs
Limitations in either capability can impede the overall performance of a VLM. A systematic evaluation of the perception and reasoning capabilities is crucial to provide valuable insights for future model optimization.
PrivCirNet: Efficient Private Inference via Block Circulant Transformation
Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead. We observe transforming the DNN weights into circulant matrices converts general matrix-vector multiplications into HE-friendly 1-dimensional convolutions, drastically reducing the HE computation cost.
Why the Learning Rate Underlying Mechanisms and Improvements
Given that warmup is standard in the practitioner's toolkit, it is important to understand it deeply and
Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
"Universalization"-based reasoning, a theory of moral thinking, are able to achieve
WizardArena: Post-training Large Language Models via Simulated Offline Chatbot Arena
Recent work demonstrates that, post-training large language models with open-domain instruction following data have achieved colossal success.
Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation Adam Fisch, Joshua Maynez, R. Alex Hofer Bhuwan Dhingra Amir Globerson William W. Cohen
Evaluating machine learning models requires evaluation data.
Non-Euclidean Mixture Model for Social Network Embedding
It is largely agreed that social network links are formed due to either homophily or social influence. Inspired by this, we aim at understanding the generation of links via providing a novel embedding-based graph formation model.
Toward a Stable, Fair, and Comprehensive Evaluation
Overcoming this challenge, existing object hallucination evaluation methods average the results obtained from a set of instructions. However, these methods fail to provide consistent evaluation across instruction sets that generate image descriptions of significantly different lengths.