Search Results for author:
Found 28 papers, 5 papers with code
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling
Event schemas can guide our understanding and ability to make predictions with respect to what might happen next.
Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations
We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.
Llama-Nemotron: Efficient Reasoning Models
We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an open license for enterprise use.
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
We introduce Nemotron-H, a family of 8B and 56B/47B hybrid Mamba-Transformer models designed to reduce inference cost for a given accuracy level.
Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset
This unlocks state-of-the-art training over a long token horizon: an 8B parameter model trained for 15T tokens, of which 7. 2T came from our dataset, is better than the Llama 3. 1 8B model: +5 on MMLU, +3. 1 on ARC-Challenge, and +0. 5 on average across ten diverse tasks.
Ranking and Combining Latent Structured Predictive Scores without Labeled Data
Combining multiple predictors obtained from distributed data sources to an accurate meta-learner is promising to achieve enhanced performance in lots of prediction problems.
FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning
Online learning has demonstrated notable potential to dynamically allocate limited resources to monitor a large population of processes, effectively balancing the exploitation of processes yielding high rewards, and the exploration of uncertain processes.
Online Modeling and Monitoring of Dependent Processes under Resource Constraints
Adaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources in many healthcare and engineering systems.
A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare
Investigating the causal relationships between characteristics and expressions plays a critical role in healthcare analytics.
Adaptive perturbation adversarial training: based on reinforcement learning
Adversarial training has become the primary method to defend against adversarial samples.
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).
Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations
For example, with "add milk to my cart", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase.
Cost-efficiency trade-offs of the human brain network revealed by a multiobjective evolutionary algorithm
Using this algorithm, we generated a population of synthetic networks with optimal but diverse cost-efficiency trade-offs.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
GAIA: A Fine-grained Multimedia Knowledge Extraction System
We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology.
A Joint Neural Model for Information Extraction with Global Features
OneIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder.
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation
In addition, we propose a two-step mention-aware attention mechanism to enable the model to focus on important words in mentions and contexts.
Reliability-aware Dynamic Feature Composition for Name Tagging
We design a set of word frequency-based reliability signals to indicate the quality of each word embedding.
Multilingual Entity, Relation, Event and Human Value Extraction
This paper demonstrates a state-of-the-art end-to-end multilingual (English, Russian, and Ukrainian) knowledge extraction system that can perform entity discovery and linking, relation extraction, event extraction, and coreference.
A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages
We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available.
A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling
We propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling.
Platforms for Non-speakers Annotating Names in Any Language
We demonstrate two annotation platforms that allow an English speaker to annotate names for any language without knowing the language.
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System
We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap.
Embracing Non-Traditional Linguistic Resources for Low-resource Language Name Tagging
Current supervised name tagging approaches are inadequate for most low-resource languages due to the lack of annotated data and actionable linguistic knowledge.
Acquiring Background Knowledge to Improve Moral Value Prediction
In this paper, we address the problem of detecting expressions of moral values in tweets using content analysis.
Distributed Differential Evolution Based on Adaptive Mergence and Split for Large-Scale Optimization
In this way, the number of subpopulations is adaptively adjusted and better performing subpopulations obtain more individuals.
List-only Entity Linking
Traditional Entity Linking (EL) technologies rely on rich structures and properties in the target knowledge base (KB).
