research

Hi everyone, I recently received an interview offer for ML position at Adyen, and the first round will be a live coding round on HackerRank. I scheduled it for the end of August because that was the l

Hi everyone, We've open-sourced Tri-Net v2, the official implementation accompanying our recently published Scientific Reports (Nature Portfolio) paper: 'Tri-Net: Unified Deep Learning for Skin Lesion

This post likely presents an enhanced demonstration of DVD-JEPA, a video variant of the Joint Embedding Predictive Architecture model. The JEPA framework has been extended to video tasks (V-JEPA) , an

TSAuditor is a contamination auditing framework for time series foundation models that uses probe adaptation dynamics to detect when evaluation datasets may have been exposed during pretraining, valid

This post likely covers the process of creating a custom drone simulation environment using MuJoCo, a physics engine commonly used in machine learning research. The project involves leveraging MuJoCo'

Gemini 3.1 Pro and Gemini 3-Pro lead visual reasoning benchmarks , with GPT-5.2, Kimi-K2.5, and GPT-5.2-Pro following . A 2026 evaluation benchmarked 15 leading multimodal models on visual reasoning a

NeurIPS 2026 used an AI detector to identify policy violations, resulting in 178 desk-rejected submissions (18.4% of all submissions) and 123 flagged for further review (12.7%) . The detection approac

Research demonstrates that LLM-based agents can generate functionally correct patches that pass all tests while still containing security vulnerabilities, challenging the assumption that test-passing

I need to check the actual content of this Reddit discussion to provide an accurate summary. Graph Neural Networks can learn environmental effects on galaxy properties, incorporating spatial relations

This research discusses a technique for enabling real-time multilingual automatic speech recognition (ASR) on edge devices by dynamically switching between compact monolingual models rather than using

Reviews and discussions for all accepted papers at ICML are made public on OpenReview after the reviewing period concludes. Authors of rejected papers may also opt-in to have their reviews and discuss

Physics-informed neural networks (PINNs) are machine learning models that incorporate physical laws and equations as constraints during training to solve differential equations. This post likely discu

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

LxMLS 2026 is a 6-day in-person event scheduled for July 20-25 at Instituto Superior Técnico that covers machine learning topics from theory to practice for solving natural language processing problem

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

A researcher from a resource-limited region discusses challenges with attending an accepted ICML workshop (GlobalSouthML) in South Korea and seeks advice on affordable attendance options, likely explo

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

A developer created a visualization tool that displays GPT-2's internal concept activations in real-time during text generation, representing the model's 'thinking' process as a 3D graph that updates

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

KDD 2026 Cycle 2 Results covers the announcement of review decisions and outcomes from the second submission cycle of KDD 2026, which accepts papers across multiple tracks including Research, Applied

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

Mechanistic interpretability research aims to uncover specific neurons and circuits in neural networks responsible for tasks, but over a decade of efforts suggests these findings may not translate int

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

A study examining 71 AI scenarios found that AI experts (N=119) and the public (N=1100) hold differing views on AI risks, benefits, and value, with experts discounting risk influences more heavily tha

The NeurIPS Creative AI track became part of the main conference proceedings for 2025, with papers presented as posters during the conference , marking a change from 2024 when the track was not part o

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

A discussion post on r/MachineLearning where users share experiences and advice about submitting machine learning research articles to ACM journals, with focus on publications like ACM Transactions on

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

The 2nd Multilingual Conversational Speech Language Model (MLC-SLM) Challenge is an open research competition inviting teams worldwide to participate , featuring free registration with a $20K prize po

The ACL Rolling Review (ARR) operates on a two-monthly review cycle , and the March 2026 cycle refers to one of these periodic submission and peer review rounds for computational linguistics research.

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 post serves as a community-curated beginner's resource for applying artificial intelligence and machine learning to materials science, likely compiling recommended to

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

This Reddit post on r/MachineLearning, tagged [R] (Research), is a community discussion thread sharing or reviewing peer feedback related to CHI PLAY — the international and interdisciplinary ACM SIGC