Search Results for author:
Found 13 papers, 3 papers with code
Nearest Neighbour Score Estimators for Diffusion Generative Models
Score function estimation is the cornerstone of both training and sampling from diffusion generative models.
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data.
Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulations in the field of autonomous driving.
Conditional Permutation Invariant Flows
We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density.
Critic Sequential Monte Carlo
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors.
MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation
We present, MATE-KD, a novel text-based adversarial training algorithm which improves the performance of knowledge distillation.
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction.
Towards Zero-Shot Knowledge Distillation for Natural Language Processing
Knowledge Distillation (KD) is a common knowledge transfer algorithm used for model compression across a variety of deep learning based natural language processing (NLP) solutions.
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping
Several methods have recently been proposed for the Single Image Super-Resolution (SISR) problem.
Time-aware Large Kernel Convolutions
Some of these models use all the available sequence tokens to generate an attention distribution which results in time complexity of $O(n^2)$.
Ranked #12 on
Machine Translation
on WMT2014 English-French
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition
Word-embeddings are vital components of Natural Language Processing (NLP) models and have been extensively explored.
Distilled embedding: non-linear embedding factorization using knowledge distillation
Word-embeddings are a vital component of Natural Language Processing (NLP) systems and have been extensively researched.
Copy this Sentence
In this paper we provide the mathematical definition of attention and examine its application to sequence to sequence models.
