3.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Nvidia (United Kingdom)GB136 papers
- Lawrence Berkeley National LaboratoryUS32 papers
- Stanford UniversityUS30 papers
- University of California, BerkeleyUS29 papers
- California Institute of TechnologyUS26 papers
- Massachusetts Institute of TechnologyUS25 papers
- Carnegie Mellon UniversityUS24 papers
- University of Illinois Urbana-ChampaignUS24 papers
- University of TorontoCA24 papers
- University of WashingtonUS23 papers
- Harvard UniversityUS19 papers
- Georgia Institute of TechnologyUS17 papers
61 papers · 1 filter
Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model
Gyujun Jeong, Sungwon Cho, Minji Shon +13
Ferroelectric field-effect transistors (FeFET)-based vertical NAND (Fe-VNAND) has emerged as a promising candidate to overcome z-scaling limitations with lower programming voltages…
TOPCELL: Topology Optimization of Standard Cell via LLMs
Zhan Song, Yu-Tung Liu, Chen Chen +6
Transistor topology optimization is a critical step in standard cell design, directly dictating diffusion sharing efficiency and downstream routability. However, identifying optima…
Surfacing Semantic Orthogonality Across Model Safety Benchmarks: A Multi-Dimensional Analysis
Jonathan Bennion, Shaona Ghosh, Mantek Singh +1
Various AI safety datasets have been developed to measure LLMs against evolving interpretations of harm. Our evaluation of five recently published open-source safety benchmarks rev…
ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration
Mengting Ai, Tianxin Wei, Yifan Chen +7
Mixture-of-Experts (MoE) Transformer, the backbone architecture of multiple phenomenal language models, leverages sparsity by activating only a fraction of model parameters for eac…
Maya: Optimizing Deep Learning Training Workloads using GPU Runtime Emulation
Srihas Yarlagadda, Amey Agrawal, Elton Pinto +6
Training large foundation models costs hundreds of millions of dollars, making deployment optimization critical. Current approaches require machine learning engineers to manually c…
Learning to Compare Hardware Designs for High-Level Synthesis
Yunsheng Bai, Atefeh Sohrabizadeh, Zijian Ding +6
High-level synthesis (HLS) is an automated design process that transforms high-level code into hardware designs, enabling the rapid development of hardware accelerators. HLS relies…
Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning
Sitao Luan, Qincheng Lu, Chenqing Hua +3
Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data. However, recent studies have found that heterophily c…
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation
Jordan Juravsky, Yunrong Guo, Sanja Fidler +1
Physically-simulated models for human motion can generate high-quality responsive character animations, often in real-time. Natural language serves as a flexible interface for cont…
CircuitVAE: Efficient and Scalable Latent Circuit Optimization
Jialin Song, Aidan Swope, Robert Kirby +4
Automatically designing fast and space-efficient digital circuits is challenging because circuits are discrete, must exactly implement the desired logic, and are costly to simulate…
General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
Yue Jian, Curtis Wu, Danny Reidenbach +1
Structure-based drug design (SBDD) aims to generate ligands that bind strongly and specifically to target protein pockets. Recent diffusion models have advanced SBDD by capturing t…
ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)
Mouadh Yagoubi, Milad Leyli-Abadi, David Danan +6
The use of machine learning (ML) techniques to solve complex physical problems has been considered recently as a promising approach. However, the evaluation of such learned physica…
Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
Ruihang Lai, Junru Shao, Siyuan Feng +16
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models. The success of these models has driven the deman…
PockEngine: Sparse and Efficient Fine-tuning in a Pocket
Ligeng Zhu, Lanxiang Hu, Ji Lin +4
On-device learning and efficient fine-tuning enable continuous and privacy-preserving customization (e.g., locally fine-tuning large language models on personalized data). However,…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Arpandeep Khatua, Vikram Sharma Mailthody, Bhagyashree Taleka +3
Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-sc…
A Text-guided Protein Design Framework
Shengchao Liu, Yanjing Li, Zhuoxinran Li +10
Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format…
Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset
Sungduk Yu, Mike Pritchard, Po-Lun Ma +2
Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic -- primarily relying on manual or grid searches.…
Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
Kaiyuan Yang, Houjing Huang, Olafs Vandans +4
A central problem in computational biophysics is protein structure prediction, i.e., finding the optimal folding of a given amino acid sequence. This problem has been studied in a…
Real-time high-resolution CO geological storage prediction using nested Fourier neural operators
Gege Wen, Zongyi Li, Qirui Long +3
Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoi…
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning
Rajarshi Roy, Jonathan Raiman, Neel Kant +6
In this work, we present a reinforcement learning (RL) based approach to designing parallel prefix circuits such as adders or priority encoders that are fundamental to high-perform…
Learning Stochastic Dynamics with Statistics-Informed Neural Network
Yuanran Zhu, Yu-Hang Tang, Changho Kim
We introduce a machine-learning framework named statistics-informed neural network (SINN) for learning stochastic dynamics from data. This new architecture was theoretically inspir…
Do Gradient Inversion Attacks Make Federated Learning Unsafe?
Ali Hatamizadeh, Hongxu Yin, Pavlo Molchanov +8
Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. This capability makes it especially interesting for healthcare application…
Contrastive prediction strategies for unsupervised segmentation and categorization of phonemes and words
Santiago Cuervo, Maciej Grabias, Jan Chorowski +4
We investigate the performance on phoneme categorization and phoneme and word segmentation of several self-supervised learning (SSL) methods based on Contrastive Predictive Coding…
Efficient GPU implementation of randomized SVD and its applications
Łukasz Struski, Paweł Morkisz, Przemysław Spurek +2
Matrix decompositions are ubiquitous in machine learning, including applications in dimensionality reduction, data compression and deep learning algorithms. Typical solutions for m…