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Found 15 papers, 2 papers with code
Representations Matter: Embedding Modes of Large Language Models using Dynamic Mode Decomposition
Existing large language models (LLMs) are known for generating "hallucinated" content, namely a fabricated text of plausibly looking, yet unfounded, facts.
Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues
Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems.
Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images
Similar to recent applications of generative models, our study suggests that diffusion models are indeed effective in generating high-quality skin images that do not sacrifice the classifier performance, and can improve the augmentation of training datasets after curation.
Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning
We study the benefits of reinforcement learning (RL) environments based on agent-based models (ABM).
On a Conjecture Regarding the Adam Optimizer
Why does the Adam optimizer work so well in deep-learning applications?
Optimizing Binary Symptom Checkers via Approximate Message Passing
Symptom checkers have been widely adopted as an intelligent e-healthcare application during the ongoing pandemic crisis.
Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithms
While the interest in the field is growing, there is a necessity for open-source libraries and toolkits to foster research and benchmark algorithms.
Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning
First, we propose a fully centralized beamforming method that uses the deep deterministic policy gradient algorithm (DDPG) with continuous space.
Multiple Access in Dynamic Cell-Free Networks: Outage Performance and Deep Reinforcement Learning-Based Design
To significantly reduce the complexity of joint processing of users' signals in presence of a large number of devices and APs, we propose a novel dynamic cell-free network architecture.
On the Adversarial Robustness of Neural Networks without Weight Transport
Neural networks trained with backpropagation, the standard algorithm of deep learning which uses weight transport, are easily fooled by existing gradient-based adversarial attacks.
Deep Learning without Weight Transport
Current algorithms for deep learning probably cannot run in the brain because they rely on weight transport, where forward-path neurons transmit their synaptic weights to a feedback path, in a way that is likely impossible biologically.
Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning
Moreover, the increased accuracy is up to 10% compared to the approach that uses the visual information provided by CNN along with a conventional decision tree-based QA system.
Hacking Google reCAPTCHA v3 using Reinforcement Learning
We present a Reinforcement Learning (RL) methodology to bypass Google reCAPTCHA v3.
Improving Skin Condition Classification with a Question Answering Model
We present a skin condition classification methodology based on a sequential pipeline of a pre-trained Convolutional Neural Network (CNN) and a Question Answering (QA) model.
TBD: Benchmarking and Analyzing Deep Neural Network Training
Our primary goal in this work is to break this myopic view by (i) proposing a new benchmark for DNN training, called TBD (TBD is short for Training Benchmark for DNNs), that uses a representative set of DNN models that cover a wide range of machine learning applications: image classification, machine translation, speech recognition, object detection, adversarial networks, reinforcement learning, and (ii) by performing an extensive performance analysis of training these different applications on three major deep learning frameworks (TensorFlow, MXNet, CNTK) across different hardware configurations (single-GPU, multi-GPU, and multi-machine).
