What Researchers Told Us at ICML: Experiment Tracking Nightmares

We Tried Training Foundation Models — From Pretraining to Eval, Fully Tracked in Neptune

Inside Bioptimus: Training (and Tracking) Foundation Models for Biology

neptune.ai Review – One source of truth for experiments | Hiren @ Vicon

neptune.ai Review – Powerful experiments comparison | Nikola @ ETH

neptune.ai Review – Neptune vs competitors | Austin @ Weill Cornell Medicine

neptune.ai Review – A Tracker for Deep Learning research | Filippo @ UiT

neptune.ai Review – Monitoring of GPU utilization | Hiren @ Vicon

neptune.ai Review – One source of truth for the team | Eugene @ ZOZO

SKEWACT: Red-Teaming LLMs via Activation-Skewed Adversarial Prompt Optimization

neptune.ai Review – Controlled access to projects | Filippo @ UiT

neptune.ai Review – Integration, onboarding & support | Hiren @ Vicon

neptune.ai Review – Logging from different environments | Aaron @ poolside

Exploration of LLM-Guided Conversation in Reminiscence Therapy

MMed-RAG: Versatile Multimodal RAG System for Med-LVLMs

neptune.ai Review – Monitoring large-scale pre-training | Carlos @ poolside

Efficient Asynchronous Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times

Optimizing Attention

Scalable Data Selection for Fine-tuning LLMs

Instruction Tuning LLMs to Understand Electronic Health Records

Demo: Fork Runs From Stable Checkpoints After Spotting Training Issues

Demo: Identify and Debug Issues in Foundation Model Traning

Demo: Monitor Large-Scale Long Training Jobs in Real Time

Demo: Find the Best Model for the Long Training (From Hundreds of Experiments)

Generating Diverse Negations from Affirmative Sentences

Instant Transformer Adaption via HyperLoRA

Interpretable Zero-Shot Predictions on Clinical Tabular Data

Adaptive LoRA Merging for Efficient Domain Incremental Learning

Realistic Threat Model for LLM Jailbreaks

Monitor & Debug Foundation Model Training – neptune.ai Demo

Adversarial Negotiation Dynamics in Generative Language Models

Advice for Teams Starting Foundation Model Training

Text-Diffusion Red-Teaming of LLMs: Unveiling Harmful Behaviors

Pushing Image Generation at Higher-Resolutions with Foundation Models

Tensor-GaLore: Memory-Efficient Training via Gradient Tensor Decomposition

Exciting Emerging Generative AI Tech

Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage

Simple Probing Evaluation of Large Multimodal Models in Medical VQA

Infecting LLM-based Multi-Agents via Self-Propagating Adversarial Attacks

Training LLMs as a Startup

What Led to the State of Foundation Model Training Report

Key Challenges and Success Factors in Foundation Model Training

LLM Training Decisions: Why Mindset is Everything

What Drives Companies to Build Their Own Foundation Models

What are Foundation Models?

Self-Stitching: Efficient Transfer Learning Using Stitching Layer

Fireside Chat on the State of Foundation Model Training Report

XLAND-100B: Large-scale Multi-task Dataset for In-context Reinforcement Learning

STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases

MLLM-CompBench: A Comparative Reasoning Benchmark for Multimodal LLMs

What’s on Horizon for Cleareye.ai

ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for LLMs

NeurIPS 2024: Impossible GenAI Questions With Karol Lasocki

Efficient Multi-Prompt Evaluation of LLMs

Growing AI Research Team at DeepL

NeurIPS 2024: Impossible GenAI Questions With Christianah Oyewale

NeurIPS 2024: Impossible GenAI Questions With Finn Schmidt

Minimising Outlier Features in Neural Network Training

Lessons From LLM Training: Insights From Genentech

NeurIPS 2024: Impossible GenAI Questions With Yinan Wang