Search Results for author:
Found 40 papers, 9 papers with code
KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
Knowledge Graph Embedding (KGE) techniques are crucial in learning compact representations of entities and relations within a knowledge graph, facilitating efficient reasoning and knowledge discovery.
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
In this paper, we propose SIMA, a framework that enhances visual and language modality alignment through self-improvement, eliminating the needs for external models or data.
Ranked #108 on
Visual Question Answering
on MM-Vet
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
To overcome this, we introduce TriSum, a framework for distilling LLMs' text summarization abilities into a compact, local model.
Bifurcated Attention: Accelerating Massively Parallel Decoding with Shared Prefixes in LLMs
This study introduces bifurcated attention, a method designed to enhance language model inference in shared-context batch decoding scenarios.
One-shot Localization and Segmentation of Medical Images with Foundation Models
Recent advances in Vision Transformers (ViT) and Stable Diffusion (SD) models with their ability to capture rich semantic features of the image have been used for image correspondence tasks on natural images.
SonoSAMTrack -- Segment and Track Anything on Ultrasound Images
In this paper, we present SonoSAMTrack - that combines a promptable foundational model for segmenting objects of interest on ultrasound images called SonoSAM, with a state-of-the art contour tracking model to propagate segmentations on 2D+t and 3D ultrasound datasets.
Exploring Continual Learning for Code Generation Models
Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance.
A Static Evaluation of Code Completion by Large Language Models
Large language models trained on code have shown great potential to increase productivity of software developers.
Greener yet Powerful: Taming Large Code Generation Models with Quantization
Such large models incur significant resource usage (in terms of memory, latency, and dollars) as well as carbon footprint.
Linear Spaces of Meanings: Compositional Structures in Vision-Language Models
These vectors can be seen as "ideal words" for generating concepts directly within the embedding space of the model.
ReCode: Robustness Evaluation of Code Generation Models
Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation.
CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context
While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i. e., in-file context, but ignore the rich semantics in other files within the same project, i. e., cross-file context, a critical source of information that is especially useful in modern modular software development.
Multi-lingual Evaluation of Code Generation Models
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
ContraCLM: Contrastive Learning For Causal Language Model
Specifically, we attain $44\%$ relative improvement on the Semantic Textual Similarity tasks and $34\%$ on Code-to-Code Search tasks.
Debiasing Neural Retrieval via In-batch Balancing Regularization
People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics.
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
Empirical analyses show that, despite the challenging nature of generative tasks, we were able to achieve a 16. 5x model footprint compression ratio with little performance drop relative to the full-precision counterparts on multiple summarization and QA datasets.
Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking
Elastic Weight Consolidation is a recently proposed method to address this issue, but scaling this approach to the modern large models used in practice requires making strong independence assumptions about model parameters, limiting its effectiveness.
Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey
This new paradigm has revolutionized the entire field of natural language processing, and set the new state-of-the-art performance for a wide variety of NLP tasks.
Improving Early Sepsis Prediction with Multi Modal Learning
Clinical text provides essential information to estimate the severity of the sepsis in addition to structured clinical data.
Neural Entity Recognition with Gazetteer based Fusion
Our gazetteer based fusion model is data efficient, achieving +1. 7 micro-F1 gains on the i2b2 dataset using 20% training data, and brings + 4. 7 micro-F1 gains on novel entity mentions never presented during training.
Zero-shot Medical Entity Retrieval without Annotation: Learning From Rich Knowledge Graph Semantics
Medical entity retrieval is an integral component for understanding and communicating information across various health systems.
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information.
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training
However, in order to achieve state-of-the-art performance on out of domain tasks such as clinical named entity recognition and relation extraction, additional in domain pre-training is required.
Improve black-box sequential anomaly detector relevancy with limited user feedback
Anomaly detectors are often designed to catch statistical anomalies.
COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature
Finally, we propose a document similarity engine that leverages low-dimensional graph embeddings from the CKG with semantic embeddings for similar article retrieval.
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature
Coronavirus disease (COVID-19) has been declared as a pandemic by WHO with thousands of cases being reported each day.
Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network
Medication name inference is the task of mapping user friendly medication names from a free-form text to a concept in a normalized medication list.
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations.
CALM: Continuous Adaptive Learning for Language Modeling
Training large language representation models has become a standard in the natural language processing community.
LATTE: Latent Type Modeling for Biomedical Entity Linking
This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS).
Towards Annotating and Creating Summary Highlights at Sub-sentence Level
Highlighting is a powerful tool to pick out important content and emphasize.
Towards Annotating and Creating Sub-Sentence Summary Highlights
Highlighting is a powerful tool to pick out important content and emphasize.
Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service
Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service launched under Amazon Web Services (AWS) trained using state-of-the-art deep learning models.
Relation Extraction using Explicit Context Conditioning
Most current RE models learn context-aware representations of the target entities that are then used to establish relation between them.
Dynamic Transfer Learning for Named Entity Recognition
We complement a standard hierarchical NER model with a general transfer learning framework consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture.
Joint Entity Extraction and Assertion Detection for Clinical Text
Most of the existing systems treat this task as a pipeline of two separate tasks, i. e., named entity recognition (NER) and rule-based negation detection.
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data.
soc2seq: Social Embedding meets Conversation Model
While liking or upvoting a post on a mobile app is easy to do, replying with a written note is much more difficult, due to both the cognitive load of coming up with a meaningful response as well as the mechanics of entering the text.
Morphological Priors for Probabilistic Neural Word Embeddings
Word embeddings allow natural language processing systems to share statistical information across related words.
Better Document-level Sentiment Analysis from RST Discourse Parsing
Discourse structure is the hidden link between surface features and document-level properties, such as sentiment polarity.
