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Found 13 papers, 3 papers with code
Repo4QA: Answering Coding Questions via Dense Retrieval on GitHub Repositories
Open-source platforms such as GitHub and Stack Overflow both play significant roles in current software ecosystems.
Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA
To address the above issues, we propose a novel self-supervised \textbf{M}ulti-level \textbf{C}ontrastive \textbf{L}earning based natural language \textbf{E}xplanation model (MCLE) for VQA with semantic-level, image-level, and instance-level factual and counterfactual samples.
Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
Experimental results on representative benchmarks demonstrate the effectiveness and robustness of MSBPG in training neural networks.
Online Tensor Learning: Computational and Statistical Trade-offs, Adaptivity and Optimal Regret
Under the fixed step size regime, a fascinating trilemma concerning the convergence rate, statistical error rate, and regret is observed.
Computationally Efficient and Statistically Optimal Robust High-Dimensional Linear Regression
The algorithm is not only computationally efficient with linear convergence but also statistically optimal, be the noise Gaussian or heavy-tailed with a finite 1 + epsilon moment.
Towards Generalized Open Information Extraction
Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts.
Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration
In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems.
A Survey on Neural Open Information Extraction: Current Status and Future Directions
Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora.
Ranked #1 on
Open Information Extraction
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Natural Language Understanding
Open-Domain Question Answering
+1
Computationally Efficient and Statistically Optimal Robust Low-rank Matrix and Tensor Estimation
Lastly, RsGrad is applicable for low-rank tensor estimation under heavy-tailed noise where a statistically optimal rate is attainable with the same phenomenon of dual-phase convergence, and a novel shrinkage-based second-order moment method is guaranteed to deliver a warm initialization.
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization
In this paper, we provide, to our best knowledge, the first theoretical guarantees of the convergence of RGrad algorithm for TT-format tensor completion, under a nearly optimal sample size condition.
Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference
Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering.
AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding
Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces.
Rule-Guided Compositional Representation Learning on Knowledge Graphs
Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces.
