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Found 10 papers, 5 papers with code
A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings
In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph.
Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channel
We find that each layer component's evaluation produces an identifiable magnetic signal signature, from which layer topology, width, function type, and sequence order can be inferred using a suitably trained classifier and a joint consistency optimization based on integer programming.
STRODE: Stochastic Boundary Ordinary Differential Equation
In this paper, we present a probabilistic ordinary differential equation (ODE), called STochastic boundaRy ODE (STRODE), that learns both the timings and the dynamics of time series data without requiring any timing annotations during training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
DeepCAD: A Deep Generative Network for Computer-Aided Design Models
We present the first 3D generative model for a drastically different shape representation --- describing a shape as a sequence of computer-aided design (CAD) operations.
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification
We introduce RP2K, a new large-scale retail product dataset for fine-grained image classification.
One Man's Trash is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples
To defend against adversarial examples, a plausible idea is to obfuscate the network's gradient with respect to the input image.
Enhancing Adversarial Defense by k-Winners-Take-All
In all cases, the robustness of k-WTA networks outperforms that of traditional networks under white-box attacks.
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach
The conventional wisdom to this end is by reducing through training a statistical distance (such as $f$-divergence) between the generated distribution and provided data distribution.
BourGAN: Generative Networks with Metric Embeddings
This paper addresses the mode collapse for generative adversarial networks (GANs).
FontCode: Embedding Information in Text Documents using Glyph Perturbation
We then introduce an algorithm that embeds a user-provided message in the text document and produces an encoded document whose appearance is minimally perturbed from the original document.
