Deep Learning

DeepMind's AlphaGenome Atlas maps every single-letter change in the human genome with 1 impact score per variant.

NVIDIA's open 30B MoE targets the agent execution layer, with Switchyard routing each step to the cheapest capable model.

This tutorial provides a comprehensive guide to building a robust sentiment analysis workflow. By combining classical TF-IDF baselines with modern parameter-efficient fine-tuning (DistilBERT + LoRA), we explore deep model interpretability, calibration, and semi-supervised techniques to achieve scalable sentiment inference

Mistral AI has released Shieldstral 1.0 3B, an open-weights, policy-adaptive multimodal safety classifier that frames content moderation as a single yes/no question instead of a fixed harm taxonomy. Operators supply the policy as a plain-language query at inference time and get back a calibrated safety score from one forward pass — no retraining required to re-target the model. Built on Ministral-3-3B-Base-2512 with a Pixtral vision encoder and trained on roughly 54.1M samples, it reports 84.9% average F1 on text safety (matching GPT-OSS-Safeguard-20B), 83.8% on multimodal safety, and 91.3% on Mistral's adaptability benchmark — while fitting in 16GB of VRAM under an Apache 2.0 license.

Meta Superintelligence Labs has released Muse Code, a terminal coding agent in beta, powered by the new Muse Spark 1.2 model. Muse Code plans changes, writes code, and validates results across large repositories. Async background agents stay active for the whole session instead of spawning per task. A local append-only event log makes the runtime replay-exact and restart-safe after a crash. Muse Spark 1.2 was co-trained with the harness and trained on long-horizon, repository-scale work.

Reflex has released XY, an Apache-2.0 Python charting library that moves rendering work into a native Rust core and a WebGL2 client. It holds roughly 0.08 seconds render time from 10,000 to 100 million points, exports a 10-million-point interactive scatter at 258 KiB, and keeps exact f64 columns in Python so hover, selection, and zoom drilldown still return original rows. The library is early alpha at version 0.0.1.

AI agents, MCP servers, and LLM apps break the core AppSec assumption that applications do what their code says. This guide walks through a practical see-fix-protect framework: a five-layer agentic AI attack surface map, a 12-point misconfiguration checklist, an evidence-based triage matrix, runtime guardrails, and system prompt hardening — plus a maturity self-assessment aligned to NIST AI RMF, OWASP AIMA, ISO/IEC 42001, and the EU AI Act.

We implement xFormers, a practical toolkit for fast, memory-efficient Transformer models on GPUs. We validate memory-efficient attention against a standard implementation, then compare speed and memory across sequence lengths. We work through causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi biases. Finally, we combine these into a trainable GPT-style model with SwiGLU layers and automatic mixed-precision training.

In this tutorial, we build an end-to-end 3D medical image segmentation pipeline using MONAI to segment the spleen on the Medical Segmentation Decathlon Task09...

We build NVIDIA Apex from source, detect fused kernels, and benchmark FusedAdam, FusedLayerNorm, and torch.amp in Transformer training.

Nous Research has published Lighthouse Attention, a selection-based hierarchical attention mechanism that wraps around standard scaled dot-product attention during pretraining and is removed afterward. Unlike prior methods such as NSA and HISA that pool only keys and values, Lighthouse pools Q, K, and V symmetrically across a multi-resolution pyramid, reducing the attention call from O(N·S·d) to O(S²·d) and running stock FlashAttention on a small dense sub-sequence. Tested on a 530M Llama-3-style model at 98K context, it achieves a 1.40–1.69× end-to-end wall-clock speedup against a cuDNN SDPA baseline with matching or lower final training loss.

When you type a message to Claude, something invisible happens in the middle. The words you send get converted into long lists of numbers...

In this tutorial, we walk through a complete, end-to-end workflow for correcting bias in survey data using the balance library. We simulate a realistic...

In this tutorial, we explore how we can decode linguistic features directly from brain signals using a modern neuroAI pipeline. We work with MEG...

Top 10 KV Cache Compression Techniques for LLM Inference: Reducing Memory Overhead Across Eviction, Quantization, and Low-Rank Methods

Mend.io's new framework gives engineering and security teams a practical playbook for governing AI systems before the next incident forces the conversation.

In this tutorial, we explore Equinox, a lightweight and elegant neural network library built on JAX, and show how to use it. We begin...

In this tutorial, we implement an advanced Bayesian hyperparameter optimization workflow using Hyperopt and the Tree-structured Parzen Estimator (TPE) algorithm. We construct a conditional...

In this tutorial, we build a pipeline on Phi-4-mini to explore how a compact yet highly capable language model can handle a full range...

OpenAI is moving from a limited pilot to broad deployment of its Trusted Access for Cyber program, introducing GPT-5.4-Cyber — a variant of GPT-5.4 fine-tuned to be cyber-permissive — for thousands of vetted defenders.

In this tutorial, we explore how to build a fully functional background task processing system using Huey directly, without relying on Redis. We configure...

OpenAI has officially entered the specialized science race with GPT-Rosalind, a frontier reasoning model designed to slash the 10-15 year timeline of drug discovery through advanced biochemistry and genomic analysis.

Learn how to combine Transformer architectures with Quantum Physics using NetKet and JAX. This guide walks through building a research-grade VMC pipeline to solve the frustrated J1-J2 Heisenberg spin chain with Neural Quantum States.

In this tutorial, we implement NVIDIA PhysicsNeMo on Colab and build a practical workflow for physics-informed machine learning. We start by setting up the...

Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or Qwen3 works through a...

Complex prediction problems often lead to ensembles because combining multiple models improves accuracy by reducing variance and capturing diverse patterns. However, these ensembles are...

Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment you move beyond plain...

In this tutorial, we take a detailed, practical approach to exploring NVIDIA’s KVPress and understanding how it can make long-context language model inference more...

In this tutorial, we implement an advanced, practical implementation of the NVIDIA Transformer Engine in Python, focusing on how mixed-precision acceleration can be explored...

Run Google’s latest omni-capable open models faster on NVIDIA RTX AI PCs, from NVIDIA Jetson Orin Nano, GeForce RTX desktops to the new DGX...

Neuroscience has long been a field of divide and conquer. Researchers typically map specific cognitive functions to isolated brain regions—like motion to area V5...

Can symbolic regression be the key to transforming opaque deep learning models into interpretable, closed-form mathematical equations? or Say you have trained your deep...

In this tutorial, we walk through advanced usage of Einops to express complex tensor transformations in a clear, readable, and mathematically precise way. We...

Earth observation (EO) constellations capture huge volumes of high-resolution imagery every day, but most of it never reaches the ground in time for model...

In this tutorial, we implement Tree-KG, an advanced hierarchical knowledge graph system that goes beyond traditional retrieval-augmented generation by combining semantic embeddings with explicit...

In this tutorial, we demonstrate a realistic data poisoning attack by manipulating labels in the CIFAR-10 dataset and observing its impact on model behavior....

Large language model serving often wastes GPU memory because engines pre-reserve large static KV cache regions per model, even when requests are bursty or...

TL;DR: Skala is a deep-learning exchange–correlation functional for Kohn–Sham Density Functional Theory (DFT) that targets hybrid-level accuracy at semi-local cost, reporting MAE ≈ 1.06...

The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception,...

Google AI, in collaboration with the UC Santa Cruz Genomics Institute, has introduced DeepPolisher, a cutting-edge deep learning tool designed to substantially improve the...

Table of contentsStep-by-Step: Mounting Google Drive in Colab (2025)Additional Tips & Updates (as of August 2025)GPU & TPU Support (2025)Summary Google Colaboratory (Colab) remains one...

Proteins are the essential component behind nearly all biological processes, from catalyzing reactions to transmitting signals within cells. While advances like AlphaFold have transformed...

The growth of data in the digital age presents both opportunities and challenges. An immense volume of text, images, audio, and video is generated...

Large Language Models (LLMs) have become an integral part of modern AI applications, powering tools like chatbots and code generators. However, the increased reliance...

Text-to-image generative models have transformed how AI interprets textual inputs to produce compelling visual outputs. These models are used across industries for applications like...

The protein design and prediction are crucial in advancing synthetic biology and therapeutics. Despite significant progress with deep learning models like AlphaFold and ProteinMPNN,...

Microsoft has released MatterSimV1-1M and MatterSimV1-5M on GitHub, cutting-edge models in materials science, offering deep-learning atomistic models tailored for precise simulations across diverse elements,...

Recommender systems (RS) are essential for generating personalized suggestions based on user preferences, historical interactions, and item attributes. These systems enhance user experience by...

Cell segmentation and classification are vital tasks in spatial omics data analysis, which provides unprecedented insights into cellular structures and tissue functions. Recent advancements...

Predicting RNA 3D structures is critical for understanding its biological functions, advancing RNA-targeted drug discovery, and designing synthetic biology applications. However, RNA's structural flexibility...

Quantum computing, despite its potential to outperform classical systems in certain tasks, faces a significant challenge: error correction. Quantum systems are highly sensitive to...

The proliferation of websites across various domains of everyday life has led to a significant rise in cybersecurity threats. The complexity and frequency of...

Drug discovery is a costly, lengthy process with high failure rates, as only one viable drug typically emerges from a million screened compounds. Advanced...

Machine learning (ML) has revolutionized wireless communication systems, enhancing applications like modulation recognition, resource allocation, and signal detection. However, the growing reliance on ML...

Understanding biomolecular interactions is crucial for fields like drug discovery and protein design. Traditionally, determining the three-dimensional structure of proteins and other biomolecules required...

Artificial intelligence systems often struggle with retaining meaningful context over extended interactions. This limitation poses challenges for applications such as chatbots and virtual assistants,...

Multi-label text classification (MLTC) assigns multiple relevant labels to a text. While deep learning models have achieved state-of-the-art results in this area, they require...

With rapid technological advances and increased internet use in business, cybersecurity has become a major global concern, especially in digital banking and payments. Digital...

DeepMind has once again taken a significant step in computational biology with the release of AlphaFold 3's inference codebase, model weights, and an on-demand...

Biomolecular dynamics simulations are crucial for life sciences, offering insights into molecular interactions. While classical molecular dynamics (MD) simulations are efficient, they lack chemical...

MicroRNAs (miRNAs) play key roles in human diseases, including cancer and infectious diseases, by regulating gene expression. Modulating miRNAs or their gene targets with...

The demand for processing power and bandwidth has increased exponentially due to the rapid advancements in Large Language Models (LLMs) and Deep Learning. The...

Designing computational workflows for AI applications, such as chatbots and coding assistants, is complex due to the need to manage numerous heterogeneous parameters, such...

Deep learning has demonstrated remarkable success across various scientific fields, showing its potential in numerous applications. These models often come with many parameters requiring...

Optimal transport is a mathematical discipline focused on determining the most efficient way to move mass between probability distributions. This field has wide-ranging applications...

Machine learning, particularly deep neural networks, focuses on developing models that accurately predict outcomes and quantify the uncertainty associated with those predictions. This dual...

Evaluating the performance of large language model (LLM) inference systems using conventional metrics presents significant challenges. Metrics such as Time To First Token (TTFT)...

Traditional protein design, often relying on physics-based methods like Rosetta, faces challenges in creating functional proteins with complex structures due to the need for...

Artificial neural networks (ANNs) traditionally lack the adaptability and plasticity seen in biological neural networks. This limitation poses a significant challenge for their application...

In solving real-world data science problems, model selection is crucial. Tree ensemble models like XGBoost are traditionally favored for classification and regression for tabular...

Machine learning research aims to learn representations that enable effective downstream task performance. A growing subfield seeks to interpret these representations' roles in model...

The rapid growth of AI and complex neural networks drives the need for efficient hardware that suits power and resource constraints. In-memory computing (IMC)...

Recent technological advancements in genomics and imaging have resulted in a vast increase in molecular and cellular profiling data, presenting challenges for traditional analysis...

Advances in deep learning have revolutionized molecule structure prediction, but real-world applications often require understanding equilibrium distributions rather than just single structures. Current methods,...

Generative AI, despite its impressive capabilities, needs to improve with slow inference speed in its real-world applications. The inference speed is how long it...

Computational biology has emerged as an indispensable discipline at the intersection of biological research & computer science, primarily focusing on biomolecular structure prediction. The...

Often perceived as abstract and challenging, physics covers fundamental aspects of the universe, from the tiny world of quantum mechanics to the vast cosmos...

Generic transport equations, comprising time-dependent partial differential equations (PDEs), delineate the evolution of extensive properties in physical systems, encompassing mass, momentum, and energy. Derived...

Multi-layer perceptrons (MLPs), or fully-connected feedforward neural networks, are fundamental in deep learning, serving as default models for approximating nonlinear functions. Despite their importance...

This study's research area is artificial intelligence (AI) and machine learning, specifically focusing on neural networks that can understand binary code. The aim is...

Global biodiversity has sharply declined in recent decades, with North America experiencing a 29% decrease in wild bird populations since 1970. Various factors drive...

MIT researchers proposed working with deep learning to address the challenges of understanding and accurately modeling the planetary boundary layer (PBL) to improve weather...

When utilizing the popular backpropagation as the default learning method, training deep neural networks—which can include hundreds of layers—can be a laborious process that...

DeepLearning AI offers a variety of short courses designed to boost your skills in generative AI and other AI technologies. These courses are crafted...

Creating deep learning architectures requires a lot of resources because it involves a large design space, lengthy prototyping periods, and expensive computations related to...

In deep learning, a unifying framework to design neural network architectures has been a challenge and a focal point of recent research. Earlier models...

Solving partial differential equations (PDEs) is complex, just like the events they explain. These equations help determine how things change over space and time,...

In an era where the demand for smarter, faster, and more efficient artificial intelligence (AI) solutions is continuously on the rise, AI21 Labs' unveiling...

Deep learning models have recently gained significant popularity in the Artificial Intelligence community. However, despite their great capacity, they frequently suffer from poor generalization....

HuggingFace Researchers introduce Quanto to address the challenge of optimizing deep learning models for deployment on resource-constrained devices, such as mobile phones and embedded...

Football has always been a game of tactical brilliance and strategic genius. From the dugouts of your local parks to the hallowed turf of...

The demand for optimized inference workloads has never been more critical in deep learning. Meet Hidet, an open-source deep-learning compiler developed by a dedicated...

In recent research, a team of researchers from Google Research has introduced FAX, an advanced software library built on top of JavaScript to improve...

The advent of large language models (LLMs) has sparked a revolution in natural language processing, captivating the world with their superior capabilities stemming from...

In the digital realm, identifying the type of files we encounter is crucial for ensuring safety and security. However, with the increasing complexity and...

The world of programming is evolving thanks to AI technologies. It is just a matter of time until artificial intelligence entirely replaces human programmers...

Value functions are a core component of deep reinforcement learning (RL). Value functions, implemented with neural networks, undergo training via mean squared error regression...

Deep Learning models have revolutionized our ability to process and understand vast amounts of data. Traditionally, these models have gravitated towards processing data in...

In the digital world, identifying the type of files we encounter is crucial for various reasons, such as ensuring user safety and maintaining security....

Underwater image processing combined with machine learning offers significant potential for enhancing the capabilities of underwater robots across various marine exploration tasks. Image segmentation,...