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Found 29 papers, 5 papers with code
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes.
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
Graph Neural Networks (GNNs), despite achieving remarkable performance across different tasks, are theoretically bounded by the 1-Weisfeiler-Lehman test, resulting in limitations in terms of graph expressivity.
Ranked #5 on
Graph Classification
on NCI109
On the Transition from Neural Representation to Symbolic Knowledge
Bridging the huge disparity between neural and symbolic representation can potentially enable the incorporation of symbolic thinking into neural networks from essence.
Adversarial Transformer Language Models for Contextual Commonsense Inference
Contextualized or discourse aware commonsense inference is the task of generating coherent commonsense assertions (i. e., facts) from a given story, and a particular sentence from that story.
cs-net: structural approach to time-series forecasting for high-dimensional feature space data with limited observations
However, when these novel methods are used to handle high-dimensional multivariate forecasting problems, their performance is highly restricted by a practical training time and a reasonable GPU memory configuration.
A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks
Persistence diagrams (PDs), often characterized as sets of death and birth of homology class, have been known for providing a topological representation of a graph structure, which is often useful in machine learning tasks.
A Study on Self-Supervised Object Detection Pretraining
In this work, we study different approaches to self-supervised pretraining of object detection models.
Collusion Detection in Team-Based Multiplayer Games
In the context of competitive multiplayer games, collusion happens when two or more teams decide to collaborate towards a common goal, with the intention of gaining an unfair advantage from this cooperation.
Non-Volatile Memory Accelerated Posterior Estimation
Bayesian inference allows machine learning models to express uncertainty.
Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis
Dimensionality reduction algorithms are frequently used to augment downstream tasks such as machine learning, data science, and also are exploratory methods for understanding complex phenomena.
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness
Although deep neural networks have achieved great performance on classification tasks, recent studies showed that well trained networks can be fooled by adding subtle noises.
Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features
Unsupervised Zero-Shot Voice Conversion (VC) aims to modify the speaker characteristic of an utterance to match an unseen target speaker without relying on parallel training data.
Revealing and Protecting Labels in Distributed Training
Prior works have demonstrated that labels can be revealed analytically from the last layer of certain models (e. g., ResNet), or they can be reconstructed jointly with model inputs by using Gradients Matching [Zhu et al'19] with additional knowledge about the current state of the model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Neural-guided, Bidirectional Program Search for Abstraction and Reasoning
More specifically, we extend existing execution-guided program synthesis approaches with deductive reasoning based on function inverse semantics to enable a neural-guided bidirectional search algorithm.
What is Learned in Knowledge Graph Embeddings?
Their relative success is often credited in the literature to their ability to learn logical rules between the relations.
RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations
Retrofitting is a technique used to move word vectors closer together or further apart in their space to reflect their relationships in a Knowledge Base (KB).
Substitutional Neural Image Compression
The key idea is to replace the image to be compressed with a substitutional one that outperforms the original one in a desired way.
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It
We show that a dropout rate of 0. 2 can reduce the speaker identity accuracy to 0% top-1 (0. 5% top-5).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge
We revisit the challenging problem of resolving prepositional-phrase (PP) attachment ambiguity.
GymFG: A Framework with a Gym Interface for FlightGear
As a result, we have developed GymFG: GymFG couples and extends a high fidelity, open-source flight simulator and a robust agent learning framework to facilitate learning of more complex tasks.
Deep Reinforcement Learning for FlipIt Security Game
We apply our model to FlipIt, a two-player security game in which both players, the attacker and the defender, compete for ownership of a shared resource and only receive information on the current state of the game upon making a move.
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks
Designing effective defense against adversarial attacks is a crucial topic as deep neural networks have been proliferated rapidly in many security-critical domains such as malware detection and self-driving cars.
Block Switching: A Stochastic Approach for Deep Learning Security
Recent study of adversarial attacks has revealed the vulnerability of modern deep learning models.
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses
However, one critical drawback of current defenses is that the robustness enhancement is at the cost of noticeable performance degradation on legitimate data, e. g., large drop in test accuracy.
Tree-Transformer: A Transformer-Based Method for Correction of Tree-Structured Data
We applied this architecture to correction tasks in both the source code and natural language domains.
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks
Based on the observations of the effect of test dropout rate on test accuracy and attack success rate, we propose a defensive dropout algorithm to determine an optimal test dropout rate given the neural network model and the attacker's strategy for generating adversarial examples. We also investigate the mechanism behind the outstanding defense effects achieved by the proposed defensive dropout.
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target domains to be bijections.
Automated software vulnerability detection with machine learning
We then compare methods applied directly to source code with methods applied to artifacts extracted from the build process, finding that source-based models perform better.
