722 citations · 5.1k across the 126 of their papers we have counts for
18 papers · 1 filter
ATT3D: Amortized Text-to-3D Object Synthesis
Jonathan Lorraine, Kevin Xie, Xiaohui Zeng +7
Text-to-3D modelling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields. DreamFusion recently achieved hig…
PADL: Language-Directed Physics-Based Character Control
Jordan Juravsky, Yunrong Guo, Sanja Fidler +1
Developing systems that can synthesize natural and life-like motions for simulated characters has long been a focus for computer animation. But in order for these systems to be use…
Optimizing Data Collection for Machine Learning
Rafid Mahmood, James Lucas, Jose M. Alvarez +2
Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect. Over-collecting dat…
Scalable Neural Data Server: A Data Recommender for Transfer Learning
Tianshi Cao, Sasha Doubov, David Acuna +1
Absence of large-scale labeled data in the practitioner's target domain can be a bottleneck to applying machine learning algorithms in practice. Transfer learning is a popular stra…
Domain Adversarial Training: A Game Perspective
David Acuna, Marc T Law, Guojun Zhang +1
The dominant line of work in domain adaptation has focused on learning invariant representations using domain-adversarial training. In this paper, we interpret this approach from a…
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks
Or Litany, Haggai Maron, David Acuna +3
Standard Federated Learning (FL) techniques are limited to clients with identical network architectures. This restricts potential use-cases like cross-platform training or inter-or…
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Tianshi Cao, Alex Bie, Arash Vahdat +2
Although machine learning models trained on massive data have led to break-throughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricte…
f-Domain-Adversarial Learning: Theory and Algorithms
David Acuna, Guojun Zhang, Marc T. Law +1
Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labele…
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Rafid Mahmood, Sanja Fidler, Marc T. Law
Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label. The large scale of data sets used in dee…
Personalized Federated Learning with First Order Model Optimization
Michael Zhang, Karan Sapra, Sanja Fidler +2
While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients. Her…
Emergent Road Rules In Multi-Agent Driving Environments
Avik Pal, Jonah Philion, Yuan-Hong Liao +1
For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow. "Road rules"…
A Theoretical Analysis of the Number of Shots in Few-Shot Learning
Tianshi Cao, Marc Law, Sanja Fidler
Few-shot classification is the task of predicting the category of an example from a set of few labeled examples. The number of labeled examples per category is called the number of…
Neural Graph Evolution: Towards Efficient Automatic Robot Design
Tingwu Wang, Yuhao Zhou, Sanja Fidler +1
Despite the recent successes in robotic locomotion control, the design of robot relies heavily on human engineering. Automatic robot design has been a long studied subject, but the…
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
Chaoqi Wang, Roger Grosse, Sanja Fidler +1
Reducing the test time resource requirements of a neural network while preserving test accuracy is crucial for running inference on resource-constrained devices. To achieve this go…
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning
Harris Chan, Yuhuai Wu, Jamie Kiros +2
Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failed experi…
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
Fartash Faghri, David J. Fleet, Jamie Ryan Kiros +1
We present a new technique for learning visual-semantic embeddings for cross-modal retrieval. Inspired by hard negative mining, the use of hard negatives in structured prediction,…
Order-Embeddings of Images and Language
Ivan Vendrov, Ryan Kiros, Sanja Fidler +1
Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate…
Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions
Jimmy Ba, Kevin Swersky, Sanja Fidler +1
One of the main challenges in Zero-Shot Learning of visual categories is gathering semantic attributes to accompany images. Recent work has shown that learning from textual descrip…