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Steal the code (ethically) and get better at ML/AI research

Analytics Vidhya

JUNE 13, 2021

The post Steal the code (ethically) and get better at ML/AI research appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon Okay! I know that stealing is a crime and we should.

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ML AI Researcher AI Research Data Science

Rethinking Reproducibility As the New Frontier in AI Research

Unite.AI

DECEMBER 20, 2023

Reproducibility, integral to reliable research, ensures consistent outcomes through experiment replication. In the domain of Artificial Intelligence (AI) , where algorithms and models play a significant role, reproducibility becomes paramount. Multiple factors contribute to the reproducibility crisis in AI research.

Steal the code (ethically) and get better at ML/AI research

Rethinking Reproducibility As the New Frontier in AI Research

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This AI Research from Stanford and UC Berkeley Discusses How ChatGPT’s Behavior is Changing Over Time

This AI Research Introduces SubGDiff: Utilizing Diffusion Model to Improve Molecular Representation Learning

This AI Research from Google DeepMind Explores the Performance Gap between Online and Offline Methods for AI Alignment

AgentLite by Salesforce AI Research: Transforming LLM Agent Development with an Open-Source, Lightweight, Task-Oriented Library for Enhanced Innovation

JP Morgan AI Research Introduces FlowMind: A Novel Machine Learning Approach that Leverages the Capabilities of LLMs such as GPT to Create an Automatic Workflow Generation System

This AI Research Introduces GAIA: A Benchmark Defining the Next Milestone in General AI Proficiency

Meta AI Researchers Open-Source Pearl: A Production-Ready Reinforcement Learning AI Agent Library

NVIDIA AI Research Releases HelpSteer: A Multiple Attribute Helpfulness Preference Dataset for STEERLM with 37k Samples

Microsoft AI Researchers Developed a New Improved Framework ResLoRA for Low-Rank Adaptation (LoRA)

This AI Research Shares a Comprehensive Overview of Large Language Models (LLMs) on Graphs

Meta AI Research Introduces MobileLLM: Pioneering Machine Learning Innovations for Enhanced On-Device Intelligence

Enhancing Autoregressive Decoding Efficiency: A Machine Learning Approach by Qualcomm AI Research Using Hybrid Large and Small Language Models

Google AI Research Introduces a Groundbreaking Quantum Algorithm for Efficiently Simulating Coupled Oscillators

Meet Guide Labs: An AI Research Startup Building Interpretable Foundation Models that can Reliably Explain their Reasoning

Navigating the Waters of Artificial Intelligence Safety: Legal and Technical Safeguards for Independent AI Research

This AI Research Presents Lucene Integration for Powerful Vector Search with OpenAI Embeddings

This AI Research from China Provides Empirical Evidence on the Relationship between Compression and Intelligence

This AI Research from Stanford Discusses Backtracing and Retrieving the Cause of the Query

This AI Research from China Explains How Common 7B Language Models Already Possess Strong Mathematical Capabilities

Researchers from Stanford University Propose MLAgentBench: A Suite of Machine Learning Tasks for Benchmarking AI Research Agents

This AI Paper Proposes ML-BENCH: A Novel Artificial Intelligence Approach Developed to Assess the Effectiveness of LLMs in Leveraging Existing Functions in Open-Source Libraries

Microsoft AI Research Introduces SIGMA: An Open-Source Research Platform to Enable Research and Innovation at the Intersection of Mixed Reality and AI

This AI Research Introduces BOFT: A New General Finetuning AI Method for the Adaptation of Foundation Models

This AI Paper Unveils the Future of MultiModal Large Language Models (MM-LLMs) – Understanding Their Evolution, Capabilities, and Impact on AI Research

Google AI Research Introduces Patchscopes: A Revolutionary AI Framework for Decoding and Enhancing the Interpretability of Large Language Models

IBM AI Research Introduces API-BLEND: A Large Corpora for Training and Systematic Testing of Tool-Augmented LLMs

LG AI Research Proposes QASA: A Novel AI Benchmark Dataset And A Computational Approach

This AI Research Presents a Physics-Based Deep Learning for Predicting IFP and Liposome Accumulation

This AI Research Presents Neural A*: A Novel Data-Driven Search Method for Path Planning Problems

Nota AI Researchers Introduce LD-Pruner: A Novel Performance-Preserving Structured Pruning Method for Compressing Latent Diffusion Models LDMs

Google AI Research Introduces ChartPaLI-5B: A Groundbreaking Method for Elevating Vision-Language Models to New Heights of Multimodal Reasoning

This AI Research Unveils Alpha-CLIP: Elevating Multimodal Image Analysis with Targeted Attention and Enhanced Control”

Researchers from Qualcomm AI Research Introduced CodeIt: Combining Program Sampling and Hindsight Relabeling for Program Synthesis

JPMorgan AI Research Introduces DocGraphLM: An Innovative AI Framework Merging Pre-Trained Language Models and Graph Semantics for Enhanced Document Representation in Information Extraction and QA

NVIDIA AI Research Introduce OpenMathInstruct-1: A Math Instruction Tuning Dataset with 1.8M Problem-Solution Pairs

This AI Research from China Proposes YAYI2-30B: A Multilingual Open-Source Large Language Model with 30 Billion Parameters

Enhancing Low-Level Visual Skills in Language Models: Qualcomm AI Research Proposes the Look, Remember, and Reason (LRR) Multi-Modal Language Model

This AI Research Introduces AstroLLaMA: A 7B Parameter Model Fine-Tuned from LLaMA-2 Using Over 300K Astronomy Abstracts From ArXiv

Cohere AI Researchers Investigate Overcoming Quantization Cliffs in Large-Scale Machine Learning Models Through Optimization Techniques

NVIDIA AI Research Proposes Language Instructed Temporal-Localization Assistant (LITA), which Enables Accurate Temporal Localization Using Video LLMs

This AI Research from Cohere AI Introduces the Mixture of Vectors (MoV) and Mixture of LoRA (MoLORA) to Mitigate the Challenges Associated with Scaling Instruction-Tuned LLMs at Scale

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