Search: “research”

This call for papers announces a workshop focused on the growing need for efficient and effective techniques for editing trained models, especially large generative models. The workshop solicits paper

This post describes implementing DCGAN (Deep Convolutional Generative Adversarial Network) inference on resource-constrained microcontroller hardware, achieving image generation with a 12.6 million pa

MergeNB is a VSCode extension to resolve Jupyter Notebook git merge conflicts . It applies three-way merge logic on matched notebook entities (source, metadata, outputs, execution_count) to automatica

Liveness detection and synthetic media detection models often fail to generalize across unseen data and struggle with content from different models. Understanding how factors like data source diversit

An OpenAI general-purpose reasoning model autonomously disproved a conjecture posed by Paul Erdos in 1946, overturning 80 years of mathematical belief. The breakthrough concerns the planar unit distan

Physics-informed neural networks (PINNs) use specialized loss functions that combine data-fitting terms with physics constraints derived from differential equations, allowing the network to learn solu

Witchcraft is a Rust reimplementation of Stanford's XTR-Warp semantic search engine that uses a single-file SQLite database for storage, enabling client-side deployment. The system operates completely

Continual Harness proposes an approach to online adaptation for foundation agents that moves beyond traditional gradient-based retraining by introducing a dual-agent architecture (Teacher/Student) wit

This paper presents a method for controllable generation in flow matching models by adapting them through reference examples. The key insight is that the velocity field in flow matching is determined

This post discusses implementing Physics-Informed Neural Networks (PINNs) to solve the 3D heat equation within a C++ WebAssembly engine for real-time aerospace applications. The work combines machine

Jensen-Shannon divergence is a method of measuring the similarity between two probability distributions. It is based on the Kullback-Leibler divergence, with the notable difference that it is symmetri

Recent advances in diffusion-based language models enable controllable sequence generation, but applying them to structured code remains challenging, prompting exploration of syntax-aware diffusion fr

ROCm has reached production-ready status for PyTorch and vLLM workloads in 2026. PyTorch runs well and MI300X benchmarks are competitive, though the ecosystem gap with CUDA remains real. AMD's MI355X

This post likely describes a machine learning project implementing QLoRA (Quantized Low-Rank Adaptation) fine-tuning on the Qwen2.5-1.5B model to classify English language proficiency levels according

This Reddit discussion covers technical challenges encountered when attempting to integrate Chebyshev filters—mathematical filters with equiripple characteristics used in signal processing—into convol

This is a discussion thread from r/MachineLearning where community members ask and answer beginner-level and straightforward questions about machine learning concepts, techniques, and practical implem

Variational Joint Embedding (VJE) is a framework that synthesizes joint embedding and variational inference to enable self-supervised learning of probabilistic representations in a reconstruction-free

This post describes a project where the author leveraged Andrej Karpathy's compact GPT implementation to create a tool for converting pandas DataFrames into searchable context windows, which they then

This Reddit discussion post addresses a beginner's question about choosing between 3B and 7B parameter models for fine-tuning on multi-task reasoning problems. The post likely contains advice from exp

INT3 compression with fused Metal kernels enables large language models to compute attention operations directly on compressed (INT3/INT4) key-value cache representations using custom GPU kernels that

Mooncake is the serving platform for Kimi developed by Moonshot AI, featuring a KVCache-centric disaggregated architecture that separates prefill and decoding clusters while leveraging underutilized C

This r/MachineLearning discussion examines a growing concern in the AI and gaming community: that certain video game 'simulators' may be deliberately designed — or repurposed — to harvest player actio

Moss is a YC-backed high-performance runtime for real-time semantic search that delivers sub-10ms lookups, instant index updates, and zero infrastructure overhead, running where the agent lives — clou

This r/MachineLearning discussion post examines LLM jailbreaks through the lens of social engineering, arguing that the psychological vulnerabilities found in LLMs are not random artifacts but structu

This Reddit post is a reminder to the r/MachineLearning community about an upcoming Ask Me Anything (AMA) session with Max Welling, Chief AI Officer at CuspAI and Professor of Machine Learning at the

This r/MachineLearning post discusses how self-hosted LLMs that comply with prompt injection attempts — effectively following malicious or overriding instructions embedded in user input — represent a

This r/MachineLearning discussion post details a hands-on benchmark study in which TranslateGemma — Google's open translation model suite built on Gemma 3, available in 4B, 12B, and 27B sizes and cove

This Reddit post from r/MachineLearning discusses the concept of decomposing machine learning models into a graph database representation, treating a model's components — such as layers, weights, and

A developer shared a project on r/MachineLearning showcasing an AI-powered tool designed to automate common data preprocessing tasks, including dataset cleaning, intelligent imputation of missing valu

A Reddit discussion thread on r/MachineLearning criticizing a procedural misstep in the ICML 2026 review process, where reviewers are newly required this year to provide a final justification describi

This Reddit discussion on r/MachineLearning explores the practical implementation details of backpropagation in Siamese networks, which consist of two or more identical subnetworks with shared weights

This Reddit thread on r/MachineLearning discusses CVPR 2026's policy requiring accepted papers to be registered under an in-person author registration, with virtual attendance still permitted if circu

A community practitioner post on r/MachineLearning documenting an experiment fine-tuning Alibaba's Qwen2.5-0.5B-Instruct model in bf16 precision on a Reddit post summarization task using GRPO (Group R

This r/MachineLearning discussion thread invites community members to share their opinions on which ML conferences or journals they consider to have the fairest and most accurate peer review processes

This Reddit post from r/MachineLearning discusses an experiment in which a transformer model was fine-tuned or prompted to encode the niche biological fact that wombats produce cube-shaped droppings —

A community-shared project posted to r/MachineLearning presenting an interactive tool for simulating and visualizing a variety of AI and ML algorithms. The tool is designed to help users build intuiti

This Reddit discussion explores the practical trade-offs of storing ArcFace face recognition embeddings in pgvector's `HALFVEC` type, which uses 16-bit floating point numbers to represent vector compo

This r/MachineLearning discussion centers on Google's TurboQuant, a training-free KV cache compression algorithm released in March 2026 that compresses cache storage from 16 bits down to 3 bits with m

This Reddit post shares an educational PyTorch repository designed to teach distributed training techniques from the ground up, covering Data Parallelism (DP), Fully Sharded Data Parallel (FSDP), Tens

A Reddit thread on r/MachineLearning discussing a community analysis of reviewer score distributions between ICLR 2025 and ICLR 2026, highlighting a dramatic year-over-year decline in scores. Submissi

The specific Reddit thread could not be retrieved directly, but based on its title, source, and the broader context of r/MachineLearning discussions, here is a fitting knowledge base summary: This r/M

This Reddit thread on r/MachineLearning is a community discussion where ICML 2025 authors share and compare their post-rebuttal reviewer scores, seeking to gauge where they stand relative to other sub

Hey Folks, I've been making quants for a while - recently I took a short break to get into hardcore research (submitted my first EMNLP paper during it!). Along the way, I built up a little arsenal of

Hey everyone ! Quick share from the cyber + local LLM side of things that I found interesting. During this week’s hacker summer camp, an AI researcher and reverse malware engineer veteran 'lordx64' on

First of all, I'm not a lab, this was a solo summer research project that finally culminated into the github repo and the writeup. The repo includes a much deeper dive with methods, findings about qua

Hi everyone, I'm a systems analysis student researching a problem a lot of you probably know well: how much you actually trust the published VRAM/RAM requirements for open-source models before trying