Government
QA: What's the Answer Right Now? Jungo Kasai Keisuke Sakaguchi Y oichi T akahashi Ronan Le Bras
We build strong baseline models upon large pretrained language models, including GPT -3 and T5. Our benchmark is an ongoing effort, and this paper presents real-time evaluation results over the past year.
A Quadratic Programming Approach to Learning Ordered Top-K Adversarial Attacks
K = 5 for all tested models, while retaining the performance for K = 1 .
Noether Embedding: Efficient Learning of Temporal Regularities Chi Gao
Learning to detect and encode temporal regularities (TRs) in events is a prerequisite for human-like intelligence. These regularities should be formed from limited event samples and stored as easily retrievable representations.
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
BERT, and GPT models show GRACE's state-of-the-art performance in making
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
However, it has several unique challenges.
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space
This paper proposes a novel contrastive learning framework, called FOCAL, for extracting comprehensive features from multimodal time-series sensing signals through self-supervised training. Existing multimodal contrastive frameworks mostly rely on the shared information between sensory modalities, but do not explicitly consider the exclusive modality information that could be critical to understanding the underlying sensing physics. Besides, contrastive frameworks for time series have not handled the temporal information locality appropriately.