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Found 13 papers, 3 papers with code
Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models
Earth system models (ESMs) are the principal tools used in climate science to generate future climate projections under various atmospheric emissions scenarios on a global or regional scale.
RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning
We explore and present how RoseNet is able to emulate the exhaustive data set using deep learning methods, and show to what extent it can predict Rosetta metrics for unseen mutant sequences with two InDels.
DiffESM: Conditional Emulation of Earth System Models with Diffusion Models
Earth System Models (ESMs) are essential tools for understanding the impact of human actions on Earth's climate.
Fine-Grained Classroom Activity Detection from Audio with Neural Networks
We obtain strong results on the new fine-grained task and state-of-the-art on the 4-way task: our best model obtains frame-level error rates of 6. 2%, 7. 7% and 28. 0% when generalizing to unseen instructors for the 4-way, 5-way, and 9-way classification tasks, respectively (relative reductions of 35. 4%, 48. 3% and 21. 6% over a strong baseline).
Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers
Many applications involve detecting and localizing specific sound events within long, untrimmed documents, including keyword spotting, medical observation, and bioacoustic monitoring for conservation.
Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks
Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate forces are going to evolve.
A Study of Few-Shot Audio Classification
In this research, we address two audio classification tasks (speaker identification and activity classification) with the Prototypical Network few-shot learning algorithm, and assess performance of various encoder architectures.
DeepClimGAN: A High-Resolution Climate Data Generator
Earth system models (ESMs), which simulate the physics and chemistry of the global atmosphere, land, and ocean, are often used to generate future projections of climate change scenarios.
Generative Adversarial Network
Vocal Bursts Intensity Prediction
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers
First, the success rate of backdoor poisoning attacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique.
Metric-Based Few-Shot Learning for Video Action Recognition
In this work, we address the task of few-shot video action recognition with a set of two-stream models.
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection.
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection
By treating system logs as threads of interleaved "sentences" (event log lines) to train online unsupervised neural network language models, our approach provides an adaptive model of normal network behavior.
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams
As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time.
