Search Results for author:
Found 33 papers, 23 papers with code
OpenAI o1 System Card
The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought.
Deliberative Alignment: Reasoning Enables Safer Language Models
As large-scale language models increasingly impact safety-critical domains, ensuring their reliable adherence to well-defined principles remains a fundamental challenge.
GPT-4o System Card
We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.
Ranked #1 on
Visual Question Answering (VQA)
on VLM2-Bench
TimeGraphs: Graph-based Temporal Reasoning
The results demonstrate both robustness and efficiency of TimeGraphs on a range of temporal reasoning tasks.
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration
Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating exceptional reasoning, tool usage, and memory capabilities.
Approximate Answering of Graph Queries
We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations.
Enabling tabular deep learning when $d \gg n$ with an auxiliary knowledge graph
However, for tabular datasets with extremely high $d$-dimensional features but limited $n$ samples (i. e. $d \gg n$), machine learning models struggle to achieve strong performance due to the risk of overfitting.
PRODIGY: Enabling In-context Learning Over Graphs
In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters.
Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
Extending the idea of graph databases (graph DBs), NGDB consists of a Neural Graph Storage and a Neural Graph Engine.
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
Deep Bidirectional Language-Knowledge Graph Pretraining
Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks.
Ranked #1 on
Riddle Sense
on RiddleSense
Inductive Logical Query Answering in Knowledge Graphs
Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes.
Few-shot Relational Reasoning via Connection Subgraph Pretraining
Our pretrained model can then be directly applied to target few-shot tasks on without the need for training few-shot tasks.
TripleE: Easy Domain Generalization via Episodic Replay
In this paper, we propose TripleE, and the main idea is to encourage the network to focus on training on subsets (learning with replay) and enlarge the data space in learning on subsets.
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering
Visual question answering (VQA) requires systems to perform concept-level reasoning by unifying unstructured (e. g., the context in question and answer; "QA context") and structured (e. g., knowledge graph for the QA context and scene; "concept graph") multimodal knowledge.
GreaseLM: Graph REASoning Enhanced Language Models for Question Answering
Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it.
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
There are two important reasoning tasks on KGs: (1) single-hop knowledge graph completion, which involves predicting individual links in the KG; and (2), multi-hop reasoning, where the goal is to predict which KG entities satisfy a given logical query.
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Here we present ConE (Cone Embedding), a KG embedding model that is able to simultaneously model multiple hierarchical as well as non-hierarchical relations in a knowledge graph.
Ranked #1 on
Ancestor-descendant prediction
on WN18RR
Ancestor-descendant prediction
Knowledge Graph Completion
+2
GreaseLM: Graph REASoning Enhanced Language Models
Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Combiner: Full Attention Transformer with Sparse Computation Cost
However, the key limitation of transformers is their quadratic memory and time complexity $\mathcal{O}(L^2)$ with respect to the sequence length in attention layers, which restricts application in extremely long sequences.
Ranked #2 on
Language Modelling
on Wiki-40B
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG.
Ranked #2 on
Riddle Sense
on RiddleSense
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.
Ranked #1 on
Knowledge Graphs
on WikiKG90M-LSC
Graph Information Bottleneck
We design two sampling algorithms for structural regularization and instantiate the GIB principle with two new models: GIB-Cat and GIB-Bern, and demonstrate the benefits by evaluating the resilience to adversarial attacks.
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
Logical operations are performed in the embedding space by neural operators over the probabilistic embeddings.
Ranked #5 on
Complex Query Answering
on NELL-995
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
We propose a variational inference framework OCEAN to perform online task inference for compositional tasks.
Open Graph Benchmark: Datasets for Machine Learning on Graphs
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Relational Message Passing for Knowledge Graph Completion
Specifically, two kinds of neighborhood topology are modeled for a given entity pair under the relational message passing framework: (1) Relational context, which captures the relation types of edges adjacent to the given entity pair; (2) Relational paths, which characterize the relative position between the given two entities in the knowledge graph.
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Our main insight is that queries can be embedded as boxes (i. e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query.
Ranked #4 on
Complex Query Answering
on FB15k-237
Bias and Generalization in Deep Generative Models: An Empirical Study
In high dimensional settings, density estimation algorithms rely crucially on their inductive bias.
Multi-Agent Generative Adversarial Imitation Learning
Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal.
Adversarial Constraint Learning for Structured Prediction
Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws.
RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment
The evaluator predicts perceptual score by extracting feature representations from the distorted and restored patches to measure GoR.
