Search Results for author:
Found 14 papers, 4 papers with code
Leveraging Large Language Models to Extract Information on Substance Use Disorder Severity from Clinical Notes: A Zero-shot Learning Approach
SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e. g., withdrawal symptoms), and social determinants of health.
Dynamic Q&A of Clinical Documents with Large Language Models
Electronic health records (EHRs) house crucial patient data in clinical notes.
Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes
We also demonstrate the QA model's ability to extract IDU-related information on temporally out-of-distribution data.
TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Machine Learning
To this end, this paper introduces TACOS, an automated synthesizer that generates topology-aware collective algorithms for common distributed machine learning collectives across arbitrary input network topologies.
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
In this paper, we extend the open-source ASTRA-sim infrastructure and endow it with the capabilities to model state-of-the-art and emerging distributed training models and platforms.
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task
We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets.
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models
Distributed training is a solution to reduce DNN training time by splitting the task across multiple NPUs (e. g., GPU/TPU).
Exploring Multi-dimensional Hierarchical Network Topologies for Efficient Distributed Training of Trillion Parameter DL Models
High-performance distributed training platforms should leverage multi-dimensional hierarchical networks, which interconnect accelerators through different levels of the network, to dramatically reduce expensive NICs required for the scale-out network.
The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations
To this end, and using those established techniques, we first developed an experimental frame-work for author detection and input perturbations.
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures
During the last two years, the goal of many researchers has been to squeeze the last bit of performance out of HPC system for AI tasks.
K-TanH: Efficient TanH For Deep Learning
We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning.
High Performance Scalable FPGA Accelerator for Deep Neural Networks
Low-precision is the first order knob for achieving higher Artificial Intelligence Operations (AI-TOPS).
Mixed Precision Training With 8-bit Floating Point
Reduced precision computation for deep neural networks is one of the key areas addressing the widening compute gap driven by an exponential growth in model size.
A Study of BFLOAT16 for Deep Learning Training
In this paper, we discuss the flow of tensors and various key operations in mixed precision training, and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16.
