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Found 13 papers, 6 papers with code
Efficient CDF Approximations for Normalizing Flows
Instead, we build upon the diffeomorphic properties of normalizing flows and leverage the divergence theorem to estimate the CDF over a closed region in target space in terms of the flux across its \emph{boundary}, as induced by the normalizing flow.
Equivariant Finite Normalizing Flows
Generative modeling seeks to uncover the underlying factors that give rise to observed data that can often be modeled as the natural symmetries that manifest themselves through invariances and equivariances to certain transformation laws.
Point Process Flows
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature.
HydraPicker: Fully Automated Particle Picking in Cryo-EM by Utilizing Dataset Bias in Single Shot Detection
Particle picking in cryo-EM is a form of object detection for noisy, low contrast, and out-of-focus microscopy images, taken of different (unknown) structures.
Parity Partition Coding for Sharp Multi-Label Classification
The problem of efficiently training and evaluating image classifiers that can distinguish between a large number of object categories is considered.
Time2Vec: Learning a Vector Representation of Time
Time is an important feature in many applications involving events that occur synchronously and/or asynchronously.
Tails of Lipschitz Triangular Flows
We investigate the ability of popular flow based methods to capture tail-properties of a target density by studying the increasing triangular maps used in these flow methods acting on a tractable source density.
Diachronic Embedding for Temporal Knowledge Graph Completion
In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time.
Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
Finally, we demonstrate how the reconstruction algorithm can be extended with an amortized inference scheme on unknown attributes such as object pose.
On the Effectiveness of Low Frequency Perturbations
Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable for safety-critical application domains.
On Learning Wire-Length Efficient Neural Networks
Pruning neural networks for wiring length efficiency is considered.
An Energy-Based Framework for Arbitrary Label Noise Correction
We propose an energy-based framework for correcting mislabelled training examples in the context of binary classification.
The Stan Math Library: Reverse-Mode Automatic Differentiation in C++
As computational challenges in optimization and statistical inference grow ever harder, algorithms that utilize derivatives are becoming increasingly more important.
Mathematical Software G.1.0; G.1.3; G.1.4; F.2.1
