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Found 19 papers, 5 papers with code
Relevant CommonSense Subgraphs for “What if...” Procedural Reasoning
We study the challenge of learning causal reasoning over procedural text to answer “What if...” questions when external commonsense knowledge is required.
BERT Enhanced Neural Machine Translation and Sequence Tagging Model for Chinese Grammatical Error Diagnosis
This paper presents the UNIPUS-Flaubert team’s hybrid system for the NLPTEA 2020 shared task of Chinese Grammatical Error Diagnosis (CGED).
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace
Uplift modeling is a rapidly growing approach that utilizes machine learning and causal inference methods to estimate the heterogeneous treatment effects.
Relevant CommonSense Subgraphs for "What if..." Procedural Reasoning
We study the challenge of learning causal reasoning over procedural text to answer "What if..." questions when external commonsense knowledge is required.
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods
Heterogeneous treatment effect (HTE) estimation with continuous treatment is essential in multiple disciplines, such as the online marketplace and pharmaceutical industry.
Relational Gating for "What If" Reasoning
We propose a novel relational gating network that learns to filter the key entities and relationships and learns contextual and cross representations of both procedure and question for finding the answer.
SRLGRN: Semantic Role Labeling Graph Reasoning Network
This work deals with the challenge of learning and reasoning over multi-hop question answering (QA).
Cross-Modality Relevance for Reasoning on Language and Vision
This work deals with the challenge of learning and reasoning over language and vision data for the related downstream tasks such as visual question answering (VQA) and natural language for visual reasoning (NLVR).
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent.
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application.
AIBench: An Industry Standard Internet Service AI Benchmark Suite
On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales.
Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking
In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues.
Performance Distributed, Parallel, and Cluster Computing
RLTM: An Efficient Neural IR Framework for Long Documents
This paper proposes a novel End-to-End neural ranking framework called Reinforced Long Text Matching (RLTM) which matches a query with long documents efficiently and effectively.
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Similarly, we show that MEGNet models trained on $\sim 60, 000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.
Ranked #3 on
Formation Energy
on Materials Project
Drug Discovery
Formation Energy
Materials Science
Computational Physics
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite
On the basis of our previous work that identifies eight data motifs taking up most of the run time of a wide variety of big data and AI workloads, we propose a scalable benchmarking methodology that uses the combination of one or more data motifs---to represent diversity of big data and AI workloads.
A Deep Learning Approach for Expert Identification in Question Answering Communities
This approach uses the convolutional neural network and combines user feature representations with question feature representations to compute scores that the user who gets the highest score is the expert on this question.
Machine Learning Based Fast Power Integrity Classifier
In this paper, we proposed a new machine learning based fast power integrity classifier that quickly flags the EM/IR hotspots.
Automated Generation and Ensemble-Learned Matching of X-ray Absorption Spectra
We report the development of XASdb, a large database of computed reference X-ray absorption spectra (XAS), and a novel Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra.
Materials Science
Customized Routing Optimization Based on Gradient Boost Regressor Model
Customized routing optimizations are applied to the URNs and results show clear timing improvement and trend to converge toward timing closure.
