DeepSeek open sources DSpark, a new framework to speed up LLM inference by up to 85%

DSpark can make decoding faster, but acceptance quality still determines how much speed the system actually realizes.
i-SCOOP

From AGI to ASI: Inside Google DeepMind’s roadmap to superintelligence

Google DeepMind's new report maps four pathways from AGI to artificial superintelligence. Here's how scaling, paradigm shifts ...
Scientific Research Publishing

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features ()

Phishing is a form of cybercrime in which people are deceived into exposing their personal information which can result in ...
GitHub

Ray Tune - Distributed Hyperparameter Tuning for Machine Learning

Ray Tune helps developers scale machine learning experiments, optimize model settings, and manage distributed training workflows efficiently. Download Ray Tune to run scalable experiment management ...
Finextra

The Hidden Cost of Hyperparameter Guessing in Financial AI

There is a quiet inefficiency sitting at the heart of almost every AI deployment in financial services today. It is not the data problem, though that remains significant. It is not the regulatory ...
IEEE

Optimizing Hyperparameter Tuning in Deep Learning Models for NLP

Abstract: Deep learning has boosted fast in the last few years. This becomes an obstacle for numerous people to efficiently make use of these assets. The complexity of complicated architecture depends ...
Nature

Hyperparameter optimization to enhance the performance of deep learning models for the early detection of invasive turtles in Korea

Invasive freshwater turtles are major drivers of biodiversity loss, underscoring the importance of early detection and management. However, it is challenging for experts to manually monitor a broad ...
Oak Ridge National Lab

DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...
Road & Track

Actor Sung Kang's Roots in the Tuning Community Run Deep

White marshmallow clouds float across the blue skies of New Jersey. It’s before noon on a late-summer Sunday, and a cool breeze rustles through the trees. Despite the abnormally pleasant weather, the ...
www.cs.cmu.edu

On Noisy Evaluation in Federated Hyperparameter Tuning

Hyperparameter tuning is critical to the success of cross-device federated learning applications. Unfortunately, federated networks face issues of scale, heterogeneity, and privacy; addressing these ...
GitHub

mohansree14/Bayesian-Deep-Learning-for-Probabilistic-Financial-Forecasting

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...
LinkedIn

Introduction to Hyperparameter Tuning in Machine Learning

Why your model’s performance might be hiding in plain sight Machine learning isn’t just about feeding data into algorithms and hoping for the best. It’s about fine-tuning—and that’s where ...