In this paper we introduce ResearchCodeAgent, a novel multi-agent system leveraging large language models (LLMs) agents to automate the codification of research methodologies described in machine learning literature. The system bridges the gap between high-level research concepts and their practical implementation, allowing researchers auto-generating code of existing research papers for benchmarking or building on top-of existing methods specified in the literature with availability of partial or complete starter code. ResearchCodeAgent employs a flexible agent architecture with a comprehensive action suite, enabling context-aware interactions with the research environment. The system incorporates a dynamic planning mechanism, utilizing both short and long-term memory to adapt its approach iteratively. We evaluate ResearchCodeAgent on three distinct machine learning tasks with distinct task complexity and representing different parts of the ML pipeline: data augmentation, optimization, and data batching. Our results demonstrate the system's effectiveness and generalizability, with 46.9% of generated code being high-quality and error-free, and 25% showing performance improvements over baseline implementations. Empirical analysis shows an average reduction of 57.9% in coding time compared to manual implementation. We observe higher gains for more complex tasks. ResearchCodeAgent represents a significant step towards automating the research implementation process, potentially accelerating the pace of machine learning research.

We introduce LeetCodeDataset, a high-quality benchmark for evaluating and training code-generation models, addressing two key challenges in LLM research: the lack of reasoning-focused coding benchmarks and self-contained training testbeds. By curating LeetCode Python problems with rich metadata, broad coverage, 100+ test cases per problem, and temporal splits (pre/post July 2024), our dataset enables contamination-free evaluation and efficient supervised fine-tuning (SFT). Experiments show reasoning models significantly outperform non-reasoning counterparts, while SFT with only 2.6K model-generated solutions achieves performance comparable to 110K-sample counterparts. The dataset and evaluation framework are available on Hugging Face and Github.

The combination of Large Language Models (LLMs), systematic evaluation, and evolutionary algorithms has enabled breakthroughs in combinatorial optimization and scientific discovery. We propose to extend this powerful combination to the control of dynamical systems, generating interpretable control policies capable of complex behaviors. With our novel method, we represent control policies as programs in standard languages like Python. We evaluate candidate controllers in simulation and evolve them using a pre-trained LLM. Unlike conventional learning-based control techniques, which rely on black box neural networks to encode control policies, our approach enhances transparency and interpretability. We still take advantage of the power of large AI models, but leverage it at the policy design phase, ensuring that all system components remain interpretable and easily verifiable at runtime. Additionally, the use of standard programming languages makes it straightforward for humans to finetune or adapt the controllers based on their expertise and intuition. We illustrate our method through its application to the synthesis of an interpretable control policy for the pendulum swing-up and the ball in cup tasks. We make the code available at https://github.com/muellerlab/synthesizing_interpretable_control_policies.git

The following post is from Neha Goel, Champion of student competitions and online data science competitions. She's here to promote a new Deep Learning challenge available to everyone. If you win, you get money, plus a bonus if you use MATLAB. We at MathWorks, in collaboration with DrivenData, are excited to bring you this challenge. Through this challenge you'll be working with a real-world dataset of drone aerial imagery (big images) for classification.

The following post is from Neha Goel, Champion of student competitions and online data science competitions. She's here to promote a new Deep Learning challenge available to everyone. If you win, you get money, plus a bonus if you use MATLAB. We at MathWorks, in collaboration with DrivenData, are excited to bring you this challenge. Through this challenge you'll be working with a real-world dataset of drone aerial imagery (big images) for classification.

Learn to apply machine learning to your problems. Follow a complete pipeline including pre-processing and training. Be able to run deep learning models with Keras on Tensorflow backend Stunning SUPPORT. I answer questions on the same day. Understand how to feed own data to deep learning models (i.e.

This starter code is implemented using PyTorch v1.0, and provides out of the box support with CUDA 9 and CuDNN 7. There are two recommended ways to set up this codebase: Anaconda or Miniconda, and Docker. Note: Docker setup is necessary if you wish to extract image features using Detectron. We provide a Dockerfile which creates a light-weight image with all the dependencies installed. We recommend this development workflow, attaching the codebase as a volume would immediately reflect source code changes inside the container environment.

Before building the chatbot, let us first understand the technology that is powering this chatbot. IBM Bluemix is a Platform As A Service cloud offering from IBM. It enables developers to quickly create, deploy, and manage applications on the cloud by abstracting other layers. Watson is a cognitive system that can understand all forms of data, interact naturally with people, and learn and derive insights out of it, at scale. Watson is available as a set of open APIs and SaaS products.