551 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- ETH ZurichCH6 papers
- Walt Disney (Switzerland)CH5 papers
- Fudan UniversityCN3 papers
- California Institute of TechnologyUS2 papers
- Carnegie Mellon UniversityUS2 papers
- Columbia UniversityUS2 papers
- Dartmouth CollegeUS2 papers
- KTH Royal Institute of TechnologySE2 papers
- University of Colorado BoulderUS2 papers
- Adobe Systems (United States)US1 paper
- AMA Computer UniversityPH1 paper
- Apple (United States)US1 paper
11 papers · 1 filter
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
Simon Kallweit, Thomas Müller, Brian McWilliams +2
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie s…
Weakly-supervised Visual Grounding of Phrases with Linguistic Structures
Fanyi Xiao, Leonid Sigal, Yong Jae Lee
We propose a weakly-supervised approach that takes image-sentence pairs as input and learns to visually ground (i.e., localize) arbitrary linguistic phrases, in the form of spatial…
Determinantal Point Processes for Mini-Batch Diversification
Cheng Zhang, Hedvig Kjellstrom, Stephan Mandt
We study a mini-batch diversification scheme for stochastic gradient descent (SGD). While classical SGD relies on uniformly sampling data points to form a mini-batch, we propose a…
Reversible Jump Metropolis Light Transport using Inverse Mappings
Benedikt Bitterli, Wenzel Jakob, Jan Novák +1
We study Markov Chain Monte Carlo (MCMC) methods operating in primary sample space and their interactions with multiple sampling techniques. We observe that incorporating the sampl…
Learning a Unified Control Policy for Safe Falling
Visak CV Kumar, Sehoon Ha, C Karen Liu
Being able to fall safely is a necessary motor skill for humanoids performing highly dynamic tasks, such as running and jumping. We propose a new method to learn a policy that mini…
Semi-Latent GAN: Learning to generate and modify facial images from attributes
Weidong Yin, Yanwei Fu, Leonid Sigal +1
Generating and manipulating human facial images using high-level attributal controls are important and interesting problems. The models proposed in previous work can solve one of t…
Weakly-Supervised Spatial Context Networks
Zuxuan Wu, Larry S. Davis, Leonid Sigal
We explore the power of spatial context as a self-supervisory signal for learning visual representations. In particular, we propose spatial context networks that learn to predict a…
Coordinated Multi-Agent Imitation Learning
Hoang M. Le, Yisong Yue, Peter Carr +1
We study the problem of imitation learning from demonstrations of multiple coordinating agents. One key challenge in this setting is that learning a good model of coordination can…
Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models
Yu-ting Qiang, Yanwei Fu, Xiao Yu +3
Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generati…
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
Patrick Wieschollek, Oliver Wang, Alexander Sorkine-Hornung +1
We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two-level pro…
Dynamic Word Embeddings
Robert Bamler, Stephan Mandt
We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by l…