Artificial Intelligence and Machine Learning
Associated Publications
Appearance-Driven Automatic 3D Model SimplificationOn the Universality of Rotation Equivariant Point Cloud Networks
Auxiliary Learning by Implicit Differentiation
Learning the Pareto Front with Hypernetworks
Learning Sparse Matrix Row Permutations for Efficient SpMM on GPU Architectures
Robust Vision-Based Cheat Detection in Competitive Gaming
Interactive Path Tracing and Reconstruction of Sparse Volumes
Deep learning-based enhancement of epigenomics data with AtacWorks
Self-Supervised Learning for Domain Adaptation on Point-Clouds
From Generalized Zero-Shot Learning to Long-Tail with Class Descriptors
NVCell: Generate Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning
Set2Graph: Learning Graphs From Sets
Online Adaptation for Consistent Mesh Reconstruction in the Wild
Self-Learning Transformations for Improving Gaze and Head Redirection
A Causal View of Compositional Zero-Shot Recognition
Neural Networks with Recurrent Generative Feedback
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning
Training Generative Adversarial Networks with Limited Data
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
Learning Deformable Tetrahedral Meshes for 3D Reconstruction
Neural Control Variates
ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Modular Primitives for High-Performance Differentiable Rendering
AV-FUZZER: Finding Safety Violations in Autonomous Driving Systems
Learning Object Permanence from Video
A Programmable Approach to Neural Network Compression
Accelerating Chip Design with Machine Learning
UFO2: A Unified Framework towards Omni-supervised Object Detection
Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
Self-supervised Single-view 3D Reconstruction via Semantic Consistency
ParaGraph: Layout Parasitics and Device Parameter Prediction using Graph Neural Networks
GRANNITE: Graph Neural Network Inference for Transferable Power Estimation
Semi-Supervised StyleGAN for Disentanglement Learning
Angular Visual Hardness
On Learning Sets of Symmetric Elements
Learning Algebraic Multigrid Using Graph Neural Networks
PyTorchFI: A Runtime Perturbation Tool for DNNs
DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement
Regularizing Neural Networks via Minimizing Hyperspherical Energy
Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection
Meshlet Priors for 3D Mesh Reconstruction
Bi3D: Stereo Depth Estimation via Binary Classifications
Analyzing and Improving the Image Quality of StyleGAN
Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths
Two-shot Spatially-varying BRDF and Shape Estimation
Self-Supervised Viewpoint Learning From Image Collections
Making Convolutions Resilient via Algorithm-Based Error Detection Techniques
Learning Canonical Representations for Scene Graph to Image Generation
Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs
Genome Variant Calling with a Deep Averaging Network
SymGAN: Orientation Estimation without Annotation for Symmetric Objects
Neurreg: Neural registration and its application to image segmentation
Domain Stylization: A Fast Covariance Matching Framework towards Domain Adaptation
A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm
PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Improved Precision and Recall Metric for Assessing Generative Models
High-Quality Self-Supervised Deep Image Denoising
Dance to Music
Few-Shot Video-to-Video Synthesis
Joint Optimization for Cooperative Image Captioning
MAGNet: A Modular Accelerator Generator for Neural Networks
Neural Inverse Rendering of an Indoor Scene from a Single Image
Few-Shot Adaptive Gaze Estimation
Confidence Regularized Self-Training
Meta-Sim: Learning to Generate Synthetic Datasets
Neural Turtle Graphics for Modeling City Road Layouts
PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
Extreme View Synthesis
Simba: Scaling Deep-Learning Inference withMulti-Chip-Module-Based Architecture
Video Stitching for Linear Camera Arrays
GPU-Accelerated Atari Emulation for Reinforcement Learning
ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection
Pixel-Adaptive Convolutional Neural Networks
A Style-Based Generator Architecture for Generative Adversarial Networks
Joint Discriminative and Generative Learning for Person Re-identification
STEP: Spatio-Temporal Progressive Learning for Video Action Detection
Semantic Image Synthesis with Spatially-Adaptive Normalization
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera
PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image
Importance Estimation for Neural Network Pruning
A 0.11 pJ/Op, 0.32-128 TOPS, Scalable Multi-Chip-Module-based Deep Neural Network Accelerator with Ground-Reference Signaling in 16nm
DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement
PRIMAL: Power Inference using Machine Learning
High Performance Graph Convolutional Networks with Applications in Testability Analysis
Analog/Mixed-Signal Hardware Error Modeling for Deep Learning Inference
DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis
Routability-Driven Macro Placement with Embedded CNN-Based Prediction Model
Informative Object Annotations: Tell Me Something I Don't Know
Metaoptimization on a Distributed System for Deep Reinforcement Learning
Unsupervised Stylish Image Description Generation via Domain Layer Norm
Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation
A Fusion Approach for Multi-Frame Optical Flow Estimation
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
Learning towards Minimum Hyperspherical Energy
RouteNet: routability prediction for mixed-size designs using convolutional neural network
Hand Pose Estimation via Latent 2.5 D Heatmap Regression
Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
Image Inpainting for Irregular Holes Using Partial Convolutions
Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
Switchable Temporal Propagation Network
A Closed-form Solution to Photorealistic Image Stylization
Multimodal Unsupervised Image-to-Image Translation
Superpixel Sampling Networks
Machine Learning and Rendering
Noise2Noise: Learning Image Restoration without Clean Data
Light-weight Head Pose Invariant Gaze Tracking
MoCoGAN: Decomposing Motion and Content for Video Generation
Improving Landmark Localization with Semi-Supervised Learning
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Deep Semantic Face Deblurring
Geometry-Aware Learning of Maps for Camera Localization
Making Convolutional Networks Recurrent for Visual Sequence Learning
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Decoupled Networks
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Learning Strict Identity Mappings in Deep Residual Networks
Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
Progressive Growing of GANs for Improved Quality, Stability, and Variation
IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification
Synchronous Multi-GPU Deep Learning with Low-Precision Communication: An Experimental Study
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
Learning Binary Residual Representations for Domain-specific Video Streaming
Learning Adaptive Parameter Tuning for Image Processing
Parallel Complexity of Forward and Backward Propagation
Machine Learning and Integral Equations
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Learning Affinity via Spatial Propagation Networks
Unsupervised Image-to-Image Translation Networks
Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications
Parallel Jaccard and Related Graph Clustering Techniques
Learning to Super-Resolve Blurry Face and Text Images
A Lightweight Approach for On-the-Fly Reflectance Estimation
Group online adaptive learning
Feedforward and Recurrent Neural Networks Backward Propagation and Hessian in Matrix Form
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion
Parallel Modularity Clustering
Pruning Convolutional Neural Networks for Resource Efficient Inference
Context-aware Captions from Context-agnostic Supervision
Learning From Noisy Large-Scale Datasets With Minimal Supervision
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
Loss Functions for Image Restoration with Neural Networks
Tensor Contractions with Extended BLAS Kernels on CPU and GPU
Temporal Ensembling for Semi-Supervised Learning
Multilayer and Multimodal Fusion of Deep Neural Networks for Video Classification
Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks
Parallel Spectral Graph Partitioning
Compressing Neural Networks with the Hashing Trick
Hand Gesture Recognition with 3D Convolutional Neural Networks
Short-Range FMCW Monopulse Radar for Hand-Gesture Sensing
Multi-sensor System for Driver’s Hand-Gesture Recognition
Adaptive Segmentation based on a Learned Quality Metric
Machine Learning for Adaptive Bilateral Filtering
cuDNN: Efficient Primitives for Deep Learning
Researchers
Aamer JaleelAaron Lefohn
Alex Keller
Ali Hatamizadeh
Andriy Myronenko
Angshuman Parashar
Anima Anandkumar
Animesh Garg
Arash Vahdat
Assaf Hallak
Avantika Lal
Balakumar Sundaralingam
Benjamin Eckart
Benjamin Klenk
Brucek Khailany
Byron Boots
Can Zhao
Chaowei Xiao
Chris Choy
Chris Paxton
Christoph Schied
Clemens Eppner
Craig Kolb
Cris Cecka
Daguang Xu
David Nellans
De-An Huang
Dieter Fox
Dong Yang
Eli Meirom
Evgeny Bolotin
Fabio Ramos
Gal Chechik
Gal Dalal
Haggai Maron
Hang Su
Holger Roth
Hongxu Danny Yin
Iuri Frosio
Jacob Munkberg
Jan Kautz
Jan Novák
Jean Kossaifi
Jeff Smith
Jim Nilsson
Joohwan Kim
Josef Spjut
Koki Nagano
Leo Tam
Marco Salvi
Mark Ren
Matthijs Van keirsbilck
Michael Stengel
Miika Aittala
Mike O'Connor
Ming-Yu Liu
Nathan Ratliff
Nathaniel Pinckney
Nikolaus Binder
Niladrish Chatterjee
Orazio Gallo
Pavlo Molchanov
Rachel Brown
Rowland O'Flaherty
Samuli Laine
Saurav Muralidharan
Shalini De Mello
Shie Mannor
Sifei Liu
Siva Hari
Stephen Tyree
Steve Keckler
Tero Karras
Thomas Müller
Timo Aila
Ting-Chun Wang
Tucker Hermans
Umar Iqbal
Vishwesh Nath
Wei Yang
Weili Nie
Wen-mei Hwu
William Dally
Wonmin Byeon
Xiaosong Wang
Yu-Wei Chao
Yuke Zhu
Yuval Atzmon
Zhiding Yu
Ziyue Xu