Successful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain. We contribute a program induction algorithm that learns a DSL while jointly training a neural network to efficiently search for programs in the learned DSL. We use our model to synthesize functions on lists, edit text, and solve symbolic regression problems, showing how the model learns a domain-specific library of program components for expressing solutions to problems in the domain.

RSL-RL is an open-source Reinforcement Learning library tailored to the specific needs of the robotics community. Unlike broad general-purpose frameworks, its design philosophy prioritizes a compact and easily modifiable codebase, allowing researchers to adapt and extend algorithms with minimal overhead. The library focuses on algorithms most widely adopted in robotics, together with auxiliary techniques that address robotics-specific challenges. Optimized for GPU-only training, RSL-RL achieves high-throughput performance in large-scale simulation environments. Its effectiveness has been validated in both simulation benchmarks and in real-world robotic experiments, demonstrating its utility as a lightweight, extensible, and practical framework to develop learning-based robotic controllers.

Summary A method for learning a DSL for program synthesis together with a search algorithm in that DSL is presented. The method proceeds iteratively, trying to solve tasks with the current DSL, and then extracting new DSL components from the solutions. Experiments show that bootstrapping the method with a DSL made up of trivial primitives is sufficient to discover common high-level constructs present in manually constructed DSLs. The paper tackles an important problem (DSL design) in an elegant and novel way. The clarity of the paper is not perfect, as the details of the idea require more space than the 8 pages available, but it clearly is stepping stone towards a new generation of program synthesis approaches.

In the modern era where software plays a pivotal role, software security and vulnerability analysis have become essential for software development. Fuzzing test, as an efficient software testing method, are widely used in various domains. Moreover, the rapid development of Large Language Models (LLMs) has facilitated their application in the field of software testing, demonstrating remarkable performance. Considering that existing fuzzing test techniques are not entirely automated and software vulnerabilities continue to evolve, there is a growing trend towards employing fuzzing test generated based on large language models. This survey provides a systematic overview of the approaches that fuse LLMs and fuzzing tests for software testing. In this paper, a statistical analysis and discussion of the literature in three areas, namely LLMs, fuzzing test, and fuzzing test generated based on LLMs, are conducted by summarising the state-of-the-art methods up until 2024. Our survey also investigates the potential for widespread deployment and application of fuzzing test techniques generated by LLMs in the future.

It has 10 tasks like retrieval, captioning, visual question answering, multimodal classification, Natural Language Visual Reasoning, Visual Dialogue, Video/Image-text Retrieval etc. It also contains 20 datasets and 30 pre-trained SOTA models for foundation language-vision models. NeMo: NVIDIA's NeMo is a is a conversational AI toolbox to work on automatic speech recognition (ASR), text-to-speech synthesis (TTS), large language models (LLMs), and natural language processing(NLP). NeMo's main goal is to assist researchers from industry and academia in reusing previous work (code and pretrained models) and to facilitate the development of new conversational AI models. Various model architectures are available for Object Detection, Instance Segmentation, Panoptic Segmentation, Contrastive Learning and Distillation. One can use existing or new datasets/models, also customize them for your problems.

By the end of this tutorial, you'll have a good introduction to the PyTorch library and be able to discuss the pros and cons of the library with other deep learning practitioners. To learn about the PyTorch deep learning library, just keep reading. PyTorch is an open source machine learning library that specializes in tensor computations, automatic differentiation, and GPU acceleration. For those reasons, PyTorch is one of the most popular deep learning libraries, competing with both Keras and TensorFlow for the prize of "most used" deep learning package: PyTorch tends to be especially popular among the research community due to its Pythonic nature and ease of extendability (i.e., implementing custom layer types, network architectures, etc.). In this tutorial, we'll discuss the basics of the PyTorch deep learning library.

Python continues to lead the way when it comes to Machine Learning, AI, Deep Learning and Data Science tasks. Because of this, we've decided to start a series investigating the top Python libraries across several categories: Of course, these lists are entirely subjective as many libraries could easily place in multiple categories. Now, let's get onto the list (GitHub figures correct as of November 16th, 2018): "pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python." "Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell (à la MATLAB or Mathematica), web application servers, and various graphical user interface toolkits."

Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade. The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry. There are various programming languages, such as JavaScript, Python, and many others, that act as a reputable entry point into the world of Machine Learning, and that brings us to the goal behind this write-up. Through this article, we will try to shed some light on more than 10 of the most popular JavaScript libraries to help you learn Machine Learning.

Successful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain. We contribute a program induction algorithm that learns a DSL while jointly training a neural network to efficiently search for programs in the learned DSL. We use our model to synthesize functions on lists, edit text, and solve symbolic regression problems, showing how the model learns a domain-specific library of program components for expressing solutions to problems in the domain. Papers published at the Neural Information Processing Systems Conference.

"I am telling you, the world's first trillionaires are going to come from somebody who masters AI and all its derivatives, and applies it in ways we never thought of." Artificial Intelligence (AI), despite being a technology that is more than 50 years old, still most people wonder what this field is all about. Even though this technology is surrounding us in many forms and we are using AI technology such as AI-based mobile apps in our day-to-day lives but still many people find it hard to believe. According to reports by this HubSpot survey, around 63% people don't realize they are using AI technologies. As Artificial Intelligence is having many associated technologies such as Machine Learning, Deep Learning, Big Data, and Natural Language Processing, etc. it's totally understandable that people get confused. If you are one among them who gets confused with all these terminologies?