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Found 30 papers, 15 papers with code
Thought Graph: Generating Thought Process for Biological Reasoning
We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processes.
SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code
SceneCraft first models a scene graph as a blueprint, detailing the spatial relationships among assets in the scene.
Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
We propose Stochastic Control Guidance (SCG), a novel guidance method that only requires forward evaluation of rule functions that can work with pre-trained diffusion models in a plug-and-play way, thus achieving training-free guidance for non-differentiable rules for the first time.
Can Large Language Model Agents Simulate Human Trust Behaviors?
In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans.
SciGLM: Training Scientific Language Models with Self-Reflective Instruction Annotation and Tuning
To bridge these gaps, we introduce SciGLM, a suite of scientific language models able to conduct college-level scientific reasoning.
TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems
Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling.
AvalonBench: Evaluating LLMs Playing the Game of Avalon
In this paper, we explore the potential of Large Language Models (LLMs) Agents in playing the strategic social deduction game, Resistance Avalon.
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
Most of the existing Large Language Model (LLM) benchmarks on scientific problem reasoning focus on problems grounded in high-school subjects and are confined to elementary algebraic operations.
A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data.
Professional Basketball Player Behavior Synthesis via Planning with Diffusion
To accomplish reward-guided trajectory generation, conditional sampling is introduced to condition the diffusion model on the value function and conduct classifier-guided sampling.
ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation
In addition, these programs can be compiled and converted into a control data flow graph (CDFG), and the compiler also provides fine-grained alignment between the code tokens and the CDFG nodes.
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory
REVEAL consists of four key components: the memory, the encoder, the retriever and the generator.
Ranked #9 on
Visual Question Answering (VQA)
on OK-VQA
Empowering Language Models with Knowledge Graph Reasoning for Question Answering
Answering open-domain questions requires world knowledge about in-context entities.
Improving Multi-Task Generalization via Regularizing Spurious Correlation
First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other.
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks
We propose a zero-shot transfer learning module for HGNNs called a Knowledge Transfer Network (KTN) that transfers knowledge from label-abundant node types to zero-labeled node types through rich relational information given in the HG.
Relation-Guided Pre-Training for Open-Domain Question Answering
Answering complex open-domain questions requires understanding the latent relations between involving entities.
Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning
Commonsense is defined as the knowledge that is shared by everyone.
Ranked #1 on
Visual Commonsense Reasoning
on GD-VCR
Cultural Vocal Bursts Intensity Prediction
Visual Commonsense Reasoning
Fuzzy Logic Based Logical Query Answering on Knowledge Graphs
Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task.
Motif-Driven Contrastive Learning of Graph Representations
Our framework MotIf-driven Contrastive leaRning Of Graph representations (MICRO-Graph) can: 1) use GNNs to extract motifs from large graph datasets; 2) leverage learned motifs to sample informative subgraphs for contrastive learning of GNN.
GPT-GNN: Generative Pre-Training of Graph Neural Networks
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.
Improving Neural Language Generation with Spectrum Control
Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks.
Heterogeneous Graph Transformer
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.
Ranked #25 on
Node Property Prediction
on ogbn-mag
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Original full-batch GCN training requires calculating the representation of all the nodes in the graph per GCN layer, which brings in high computation and memory costs.
Demystifying Graph Neural Network Via Graph Filter Assessment
However, there still lacks in-depth analysis on (1) Whether there exists a best filter that can perform best on all graph data; (2) Which graph properties will influence the optimal choice of graph filter; (3) How to design appropriate filter adaptive to the graph data.
Learning to Transfer via Modelling Multi-level Task Dependency
Besides, the understanding of relationships among tasks has been ignored by most of the current methods.
Few-Shot Representation Learning for Out-Of-Vocabulary Words
Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts.
Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
With the proposed pre-training procedure, the generic structural information is learned and preserved, thus the pre-trained GNN requires less amount of labeled data and fewer domain-specific features to achieve high performance on different downstream tasks.
Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm
Although click data is widely used in search systems in practice, so far the inherent bias, most notably position bias, has prevented it from being used in training of a ranker for search, i. e., learning-to-rank.
Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification
To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i. e., the source language, usually English) to another language with fewer labels (i. e., the target language).
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Stock trend prediction plays a critical role in seeking maximized profit from stock investment.
Ranked #16 on
Stock Market Prediction
on Astock
