convex hull
- North America > United States > New York (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Europe > Germany > Brandenburg > Potsdam (0.04)
Exploring Geometry of Blind Spots in Vision Models
We propose a Level Set Traversal algorithm that iteratively explores regions of high confidence with respect to the input space using orthogonal components of the local gradients.
- North America > United States > Maryland > Prince George's County > College Park (0.04)
- Asia > Middle East > Jordan (0.04)
We would like to thank the reviewers for their constructive feedbacks and we will correct the typos raised and include
Full (exact) conformal set vs. split or cross-validated conformal set Non-connectedness of the conformal prediction set. This was initially suggested in [18, Remark 1]. We follow the actual practice in the literature [14, Remark 5]. We did not observe violations. We will also summarize the proposed algorithm in a direct pseudo-code.
Limiting Extrapolation in Linear Approximate Value Iteration
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Neural Information Processing Systems http://nips.cc/
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- Oceania > Australia > New South Wales > Sydney (0.04)
- North America > United States > Washington > King County > Bellevue (0.04)
- (3 more...)
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families
Brian Axelrod, Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, Gregory Valiant
Neural Information Processing Systems http://nips.cc/
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.14)
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- North America > United States > Wisconsin > Dane County > Madison (0.04)
- (4 more...)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.72)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.72)
Partition-BasedFormulationsforMixed-Integer OptimizationofTrainedReLUNeuralNetworks
This paper introduces a class of mixed-integer formulations for trained ReLU neuralnetworks.