Search Results for author:
Found 28 papers, 9 papers with code
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.
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
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.
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.
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.
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 #4 on
Formation Energy
on Materials Project
Drug Discovery
Formation Energy
Materials Science
Computational Physics
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.
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
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.
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 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.
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).
SRLGRN: Semantic Role Labeling Graph Reasoning Network
This work deals with the challenge of learning and reasoning over multi-hop question answering (QA).
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.
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.
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace
We show the effectiveness of GCF by deriving the asymptotic property of the estimator and comparing it to popular uplift modeling methods on both synthetic and real-world datasets.
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.
Long-term Causal Effects Estimation via Latent Surrogates Representation Learning
Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e. g., marketing and medicine.
Dynamic Relevance Graph Network for Knowledge-Aware Question Answering
DRGN operates on a given KG subgraph based on the question and answers entities and uses the relevance scores between the nodes to establish new edges dynamically for learning node representations in the graph network.
Point-Voxel Adaptive Feature Abstraction for Robust Point Cloud Classification
Specifically, the proposed framework iteratively voxelize the point cloud and extract point-voxel feature with shared local encoding and Transformer.
GLUECons: A Generic Benchmark for Learning Under Constraints
Recent research has shown that integrating domain knowledge into deep learning architectures is effective -- it helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models.
Improving the Generalization Ability in Essay Coherence Evaluation through Monotonic Constraints
To address these concerns, we propose a coherence scoring model consisting of a regression model with two feature extractors: a local coherence discriminative model and a punctuation correction model.
Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy
This paper introduces a multifaceted methodology for fine-tuning and evaluating large language models (LLMs) for specialized monetization tasks.
A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest
This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources, and the adaptive training of a chatbot with domain-specific inquiries.
ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers
The emergence of Large Language Models (LLMs) such as ChatGPT and LLaMA encounter limitations in domain-specific tasks, with these models often lacking depth and accuracy in specialized areas, and exhibiting a decrease in general capabilities when fine-tuned, particularly analysis ability in small sized models.
Balancing Enhancement, Harmlessness, and General Capabilities: Enhancing Conversational LLMs with Direct RLHF
This process often leads to issues such as forgetting or a decrease in the base model's abilities.
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).
