Search Results for author:
Found 16 papers, 4 papers with code
Improvements & Evaluations on the MLCommons CloudMask Benchmark
In this paper, we report the performance benchmarking results of deep learning models on MLCommons' Science cloud-masking benchmark using a high-performance computing cluster at New York University (NYU): NYU Greene.
Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale
In this paper, we study the aggregate effect of power-capping GPUs on GPU temperature and power draw at a research supercomputing center.
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models
Experiments show that QST can reduce the total memory footprint by up to 2. 3 $\times$ and speed up the finetuning process by up to 3 $\times$ while achieving competent performance compared with the state-of-the-art.
From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference
Large language models (LLMs) have exploded in popularity due to their new generative capabilities that go far beyond prior state-of-the-art.
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks
Works on implicit regularization have studied gradient trajectories during the optimization process to explain why deep networks favor certain kinds of solutions over others.
A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics
This paper proposes a special-purpose system to achieve high-accuracy and high-efficiency machine learning (ML) molecular dynamics (MD) calculations.
Energy-Efficient Blockchain-enabled User-Centric Mobile Edge Computing
To achieve efficient and reliable resource utilization with user-centric services, we propose an energy efficient blockchain-enabled UC-MEC system where blockchain operations and resource optimization are jointly performed.
A Green(er) World for A.I
applications, we are sure to face an ever-mounting energy footprint to sustain these computational budgets, data storage needs, and more.
AI Enabled Maneuver Identification via the Maneuver Identification Challenge
Artificial intelligence (AI) has enormous potential to improve Air Force pilot training by providing actionable feedback to pilot trainees on the quality of their maneuvers and enabling instructor-less flying familiarization for early-stage trainees in low-cost simulators.
From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model
Accurate prediction of the protein-ligand binding affinities is an essential challenge in the structure-based drug design.
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction
In this paper, we argue that there exist two major issues hindering current self-supervised learning methods from obtaining desired performance on molecular property prediction, that is, the ill-defined pre-training tasks and the limited model capacity.
Graph Representation Learning
Molecular Property Prediction
+2
Benchmarking Resource Usage for Efficient Distributed Deep Learning
Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains.
Ionic thermoelectric materials and devices
In this review, we summarize the state of the art in the new field of ionic thermoelectrics, from the driving force of the ionic thermodiffusion to material and application developments.
Materials Science
Deep Learning in Computer-Aided Diagnosis and Treatment of Tumors: A Survey
Computer-Aided Diagnosis and Treatment of Tumors is a hot topic of deep learning in recent years, which constitutes a series of medical tasks, such as detection of tumor markers, the outline of tumor leisures, subtypes and stages of tumors, prediction of therapeutic effect, and drug development.
Joint Passive Beamforming and User Association Optimization for IRS-assisted mmWave Systems
Considering the impact of IRS on user association, we formulate a sum rate maximization problem by jointly optimizing the passive beamforming at IRS and user association, which is an intractable non-convex problem.
Energy Efficiency Optimization in IRS-Enhanced mmWave Systems with Lens Antenna Array
To address this problem, we propose an intelligent reflect surface (IRS) enhanced multi-user mmWave communication system with lens antenna array.
