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Found 23 papers, 10 papers with code
Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications
Driving scenario data play an increasingly vital role in the development of intelligent vehicles and autonomous driving.
Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence
Artificial intelligence (AI) is transforming scientific research, including proteomics.
Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents
We advocate model-based planning for web agents that employs a world model to simulate and deliberate over the outcome of each candidate action before committing to one.
AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG
A significant portion of the effort is experience-driven, which makes the automation of AMS circuit design a formidable challenge.
Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents
The key is visual grounding models that can accurately map diverse referring expressions of GUI elements to their coordinates on the GUI across different platforms.
Ranked #1 on
Natural Language Visual Grounding
on ScreenSpot
Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles
To address this, we develop Roleplay-doh, a novel human-LLM collaboration pipeline that elicits qualitative feedback from a domain-expert, which is transformed into a set of principles, or natural language rules, that govern an LLM-prompted roleplay.
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models
Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks.
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.
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.
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.
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.
