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Found 25 papers, 10 papers with code
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc.
In-situ Self-optimization of Quantum Dot Emission for Lasers by Machine-Learning Assisted Epitaxy
In this work, we integrate in-situ reflection high-energy electron diffraction (RHEED) with machine learning (ML) to correlate the surface reconstruction with the photoluminescence (PL) of InAs/GaAs quantum dots (QDs), which serve as the active region of lasers.
Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks
Causal discovery from time-series data aims to capture both intra-slice (contemporaneous) and inter-slice (time-lagged) causality between variables within the temporal chain, which is crucial for various scientific disciplines.
SemiEpi: Self-driving, Closed-loop Multi-Step Growth of Semiconductor Heterostructures Guided by Machine Learning
The semiconductor industry has prioritized automating repetitive tasks through closed-loop, self-driving experimentation, accelerating the optimization of complex multi-step processes.
Returning to the Start: Generating Narratives with Related Endpoints
Human writers often bookend their writing with ending sentences that relate back to the beginning sentences in order to compose a satisfying narrative that "closes the loop."
SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization
Multi-turn dialogues are characterized by their extended length and the presence of turn-taking conversations.
Universal Deoxidation of Semiconductor Substrates Assisted by Machine-Learning and Real-Time-Feedback-Control
It standardizes deoxidation temperatures across various equipment and substrate materials, advancing the standardization research process in semiconductor preparation, a significant milestone in thin film growth technology.
Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots
Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources.
"What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge
We hope this task and dataset can promote further research on TOD and subjective content understanding.
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset.
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach
In particular, we hypothesize that the order of the input concepts can affect the PTM's ability to utilize its commonsense knowledge.
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension
Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances, which are either scattered around or implicitly implied in different turns of conversations.
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document.
Unsupervised Extractive Opinion Summarization Using Sparse Coding
A semantic unit is supposed to capture an abstract semantic concept.
Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning
Document-level Relation Extraction (DRE) aims to recognize the relations between two entities.
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"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc.
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation
Generating sequential natural language descriptions from graph-structured data (e. g., knowledge graph) is challenging, partly because of the structural differences between the input graph and the output text.
Weakly-Supervised Opinion Summarization by Leveraging External Information
Opinion summarization from online product reviews is a challenging task, which involves identifying opinions related to various aspects of the product being reviewed.
Deep Dexterous Grasping of Novel Objects from a Single View
First, we present a simulator for generating and testing dexterous grasps.
Neural Entropic Estimation: A faster path to mutual information estimation
In particular, we show that MI-NEE reduces to MINE in the special case when the reference distribution is the product of marginal distributions, but faster convergence is possible by choosing the uniform distribution as the reference distribution instead.
Classification of entities via their descriptive sentences
Hypernym identification of open-domain entities is crucial for taxonomy construction as well as many higher-level applications.
De-identification of medical records using conditional random fields and long short-term memory networks
The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing focuses on the de-identification of psychiatric evaluation records.
Constructing a Hierarchical User Interest Structure based on User Profiles
In this study, we constructed the user interest hierarchy via user profiles.
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Our objective is a general system that can extract and represent these knowledge contained in EMRs to support three CDS tasks: test recommendation, initial diagnosis, and treatment plan recommendation, with the given condition of one patient.
Learning and inference in knowledge-based probabilistic model for medical diagnosis
Based on a weighted knowledge graph to represent first-order knowledge and combining it with a probabilistic model, we propose a methodology for the creation of a medical knowledge network (MKN) in medical diagnosis.
