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Found 11 papers, 2 papers with code
HetTree: Heterogeneous Tree Graph Neural Network
In this paper, we present HetTree, a novel heterogeneous tree graph neural network that models both the graph structure and heterogeneous aspects in a scalable and effective manner.
RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution
Finally, we discuss the limitation and the future directions of the deep neural network-based perception algorithms and autonomous driving dataset generation based on image-to-image translation.
PAC Neural Prediction Set Learning to Quantify the Uncertainty of Generative Language Models
Uncertainty learning and quantification of models are crucial tasks to enhance the trustworthiness of the models.
Understanding Contrastive Learning Through the Lens of Margins
In this work, we propose a new perspective to understand the role of margins based on gradient analysis.
Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images
Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the breast over time.
ACon$^2$: Adaptive Conformal Consensus for Provable Blockchain Oracles
To address the oracle problem, we propose an adaptive conformal consensus (ACon$^2$) algorithm that derives a consensus set of data from multiple oracle contracts via the recent advance in online uncertainty quantification learning.
Unsafe's Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering via Machine Learning
Memory-safety bugs introduce critical software-security issues.
OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs
We validate our approach using a large scale proprietary dataset of over 100K radiographs as well as publicly available RANZCR-CLiP Kaggle Challenge dataset and show that our method consistently outperforms commonly used regression-based detection models as well as commonly used pixel-wise classification methods.
ADA-VAD: Unpaired Adversarial Domain Adaptation for Noise-Robust Voice Activity Detection
In this paper, we propose adversarial domain adaptive VAD (ADA-VAD), which is a deep neural network (DNN) based VAD method highly robust to audio samples with various noise types and low SNRs.
Ranked #4 on
Activity Detection
on AVA-Speech
(ROC-AUC metric)
Semantic-aware Binary Code Representation with BERT
Recently, binary analysis techniques based on machine learning have been proposed to automatically reconstruct the code representation of a binary instead of manually crafting specifics of the analysis algorithm.
RECIPE : Converting Concurrent DRAM Indexes to Persistent-Memory Indexes
We present Recipe, a principled approach for converting concurrent DRAM indexes into crash-consistent indexes for persistent memory (PM).
Distributed, Parallel, and Cluster Computing Databases Data Structures and Algorithms
