Search Results for author:
Found 11 papers, 3 papers with code
SimLM: Can Language Models Infer Parameters of Physical Systems?
Several machine learning methods aim to learn or reason about complex physical systems.
Generating Parametric BRDFs from Natural Language Descriptions
Since the output of our model is a parametric BRDF, rather than an image of the material, it may be used to render materials using any shape under arbitrarily specified viewing and lighting conditions.
Dist2Cycle: A Simplicial Neural Network for Homology Localization
Simplicial complexes can be viewed as high dimensional generalizations of graphs that explicitly encode multi-way ordered relations between vertices at different resolutions, all at once.
PDBench: Evaluating Computational Methods for Protein Sequence Design
We compare five existing models with two novel models for sequence prediction.
IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates
This distribution can then be used as a policy certificate in downstream applications.
Action sequencing using visual permutations
Humans can easily reason about the sequence of high level actions needed to complete tasks, but it is particularly difficult to instil this ability in robots trained from relatively few examples.
Q-NET: A Network for Low-Dimensional Integrals of Neural Proxies
The use of a \emph{proxy} or surrogate for the true function is useful if repeated evaluations are necessary.
WhoAmI: An Automatic Tool for Visual Recognition of Tiger and Leopard Individuals in the Wild
We develop automatic algorithms that are able to detect animals, identify the species of animals and to recognize individual animals for two species.
Learning rewards for robotic ultrasound scanning using probabilistic temporal ranking
This work considers the inverse problem, where the goal of the task is unknown, and a reward function needs to be inferred from exploratory example demonstrations provided by a demonstrator, for use in a downstream informative path-planning policy.
Automatic Generation of Constrained Furniture Layouts
Efficient authoring of vast virtual environments hinges on algorithms that are able to automatically generate content while also being controllable.
Fully-Connected CRFs with Non-Parametric Pairwise Potential
Conditional Random Fields (CRFs) are used for diverse tasks, ranging from image denoising to object recognition.
