We introduce beta diffusion, a novel generative modeling method that
int...
Learning to denoise has emerged as a prominent paradigm to design
state-...
Dynamic control flow is an important technique often used to design
expr...
Converging evidence indicates that the heterogeneity of cognitive profil...
Batching has a fundamental influence on the efficiency of deep neural ne...
There is a growing need to deploy machine learning for different tasks o...
Sparse tensors are rapidly becoming critical components of modern deep
l...
Deploying deep learning models on various devices has become an importan...
Automatic optimization for tensor programs becomes increasingly importan...
Brain functional connectome, the collection of interconnected neural cir...
Strong demands for efficient deployment of Deep Learning (DL) applicatio...
There is often variation in the shape and size of input data used for de...
Recent works have theoretically and empirically shown that deep neural
n...
K-Nearest Neighbor (kNN)-based deep learning methods have been applied t...
Biomimetics has played a key role in the evolution of artificial neural
...
Quantization is a key technique to reduce the resource requirement and
i...
Adversarial attacks against deep neural networks are continuously evolvi...
Optimizing deep learning models is generally performed in two steps: (i)...
Checkpointing enables training deep learning models under restricted mem...
Frameworks for writing, compiling, and optimizing deep learning (DL) mod...
Frameworks for writing, compiling, and optimizing deep learning (DL) mod...
Virtual execution environments allow for consolidation of multiple
appli...
State of the art deep learning models have made steady progress in the f...
Machine learning powers diverse services in industry including search,
t...
Hardware acceleration is an enabler for ubiquitous and efficient deep
le...
We introduce a learning-based framework to optimize tensor programs for ...
There is an increasing need to bring machine learning to a wide diversit...
Scalable frameworks, such as TensorFlow, MXNet, Caffe, and PyTorch drive...
MXNet is a multi-language machine learning (ML) library to ease the
deve...
We introduce techniques for rapidly transferring the information stored ...
Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous...
In this paper we investigate the performance of different types of recti...
Many tasks in data mining and related fields can be formalized as matchi...
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for
d...
Recommender system has been more and more popular and widely used in man...