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Bayesian Model-Agnostic Meta-Learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn
A robust meta-learning algorithm therefore mustbe able to systematically deal with such uncertainty in order to be applicable to critical problemssuch as healthcare and self-driving cars.
Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning
We give a model of how to infer natural language rules by doing experiments.
Learning to Optimize in Swarms
Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen
Neural Information Processing Systems http://nips.cc/
Adversarial Music: Real world Audio Adversary against Wake-word Detection System
Juncheng Li, Shuhui Qu, Xinjian Li, Joseph Szurley, J. Zico Kolter, Florian Metze
Neural Information Processing Systems http://nips.cc/
ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models
We contend that utilizing existing relational databases is a promising approach to construct a benchmark that has both merits.
Quilt-1M: One Million Image-Text Pairs for Histopathology Wisdom O. Ikezogwo
Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online.
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation
Yuan Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing
Neural Information Processing Systems http://nips.cc/
Acceleration through Optimistic No-Regret Dynamics
Jun-Kun Wang, Jacob D. Abernethy
Zero-sum games can be solved using online learning dynamics, where a classical technique involves simulating two no-regret algorithms that play against each other and, afterT rounds, the average iterate is guaranteed to solve the original optimization problem with error decaying asO(logT/T). In this paper we show that the technique can be enhanced to a rate ofO(1/T2) by extending recent work [22, 25] that leverages optimistic learning to speed upequilibrium computation.
Face Reconstruction from Voice using Generative Adversarial Networks
Yandong Wen, Bhiksha Raj, Rita Singh
Neural Information Processing Systems http://nips.cc/