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Found 16 papers, 7 papers with code
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing
De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research.
A conservative hybrid physics-informed neural network method for Maxwell-Ampère-Nernst-Planck equations
However, when the spatial dimension is one, the original curl-free relaxation component is inapplicable, and the approximation formula for dummy variables, which works well in a 2-dimensional scenario, fails to provide a reasonable output in the 1-dimensional case.
Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication
Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique.
Introducing π-HelixNovo for practical large-scale de novo peptide sequencing
De novo peptide sequencing is a promising approach for novel peptide discovery.
Secrets of RLHF in Large Language Models Part I: PPO
Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model.
A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification
The proposed model learns from both data and physics constraints through the training of a deep neural network, which serves as part of the covariance function in GPR.
Modeling the US-China trade conflict: a utility theory approach
This paper models the US-China trade conflict and attempts to analyze the (optimal) strategic choices.
Learning Effective Visual Relationship Detector on 1 GPU
We present our winning solution to the Open Images 2019 Visual Relationship challenge.
Guided Similarity Separation for Image Retrieval
Despite recent progress in computer vision, image retrieval remains a challenging open problem.
Semi-Supervised Exploration in Image Retrieval
We present our solution to Landmark Image Retrieval Challenge 2019.
Explore-Exploit Graph Traversal for Image Retrieval
Given a nearest neighbor graph produced by the global descriptor model, we traverse it by alternating between exploit and explore steps.
Cost-Sensitive Active Learning for Dialogue State Tracking
Dialogue state tracking (DST), when formulated as a supervised learning problem, relies on labelled data.
Real-time deep hair matting on mobile devices
Augmented reality is an emerging technology in many application domains.
Affordable On-line Dialogue Policy Learning
The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical.
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning
Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy.
On-line Dialogue Policy Learning with Companion Teaching
On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios.
