Posted by Malaya Jules, Program Manager, Google This week, the 61st annual meeting of the Association for Computational Linguistics (ACL), a premier conference covering a broad spectrum of research areas that are concerned with computational approaches to natural language, is taking place online. As a… Read More »Google at ACL 2023 Google AI Google AI Blog
Home Table of Contents Introduction to Autoencoders What Are Autoencoders? How Autoencoders Achieve High-Quality Reconstructions? Revisiting the Story Types of Autoencoder Vanilla Autoencoder Convolutional Autoencoder (CAE) Denoising Autoencoder Sparse Autoencoder Variational Autoencoder (VAE) Sequence-to-Sequence Autoencoder What Are the Applications of Autoencoders? Dimensionality Reduction Feature… Read More »Introduction to Autoencoders Aditya Sharma PyImageSearch
Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase in dimensionality (bottom left). Direct visualization of embeddings (right; top three PCA directions shown) confirms that embeddings are initially collapsed to a point, which then expands to a 1D manifold, a 2D manifold, and beyond concurrently with steps in the loss.
It is widely believed that deep learning’s stunning success is due in part to its ability to discover and extract useful representations of complex data. Self-supervised learning (SSL) has emerged as a leading framework for learning these representations for images directly from unlabeled data, similar to how LLMs learn representations for language directly from web-scraped text. Yet despite SSL’s key role in state-of-the-art models such as CLIP and MidJourney, fundamental questions like “what are self-supervised image systems really learning?” and “how does that learning actually occur?” lack basic answers.
Our recent paper (to appear at ICML 2023) presents what we suggest is the first compelling mathematical picture of the training process of large-scale SSL methods. Our simplified theoretical model, which we solve exactly, learns aspects of the data in a series of discrete, well-separated steps. We then demonstrate that this behavior can be observed in the wild across many current state-of-the-art systems.
This discovery opens new avenues for improving SSL methods, and enables a whole range of new scientific questions that, when answered, will provide a powerful lens for understanding some of today’s most important deep learning systems.
Researchers have made notable strides in training diffusion models using reinforcement learning (RL) to enhance prompt-image alignment and optimize various objectives. Introducing denoising diffusion policy optimization (DDPO), which treats denoising diffusion as a multi-step decision-making problem, enables fine-tuning Stable Diffusion on challenging downstream objectives.… Read More »UC Berkeley And MIT Researchers Propose A Policy Gradient Algorithm Called Denoising Diffusion Policy Optimization (DDPO) That Can Optimize A Diffusion Model For Downstream Tasks Using Only A Black-Box Reward Function Niharika Singh Artificial Intelligence Category – MarkTechPost
Tongyi Wanxiang (‘Wanxiang’ means ‘tens of thousands of photos’) is the latest AI image creation model announced by Alibaba Cloud, the digital technology and intelligence backbone of the Alibaba Group, during the World Artificial Intelligence Conference 2023. Enterprise customers in China can now participate… Read More »Alibaba Cloud Unveils Tongyi Wanxiang: An AI Image Generation Model to Help Businesses to Unleash Creativity and Productivity Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost
GIFs are a fantastic choice if you’re searching for a fun and original approach to spice up your web material. With the development of artificial intelligence gif generators, making professional-grade animations without effort is possible. This article looks closely at several of the top… Read More »Best AI GIF Generators (2023) Prathamesh Ingle Artificial Intelligence Category – MarkTechPost
The task of building machine-learning models can be challenging, particularly for researchers without expertise in machine learning. However, a team of researchers at MIT has developed an innovative solution called BioAutoMATED. This automated machine-learning system streamlines the process of model selection and data preprocessing,… Read More »MIT Researchers Developed BioAutoMATED: An Automated Machine-Learning System That Can Select And Build An Appropriate Model For A Given Dataset Niharika Singh Artificial Intelligence Category – MarkTechPost
Successful reinforcement learning (RL) applications include difficult tasks like plasma control, molecular design, game playing, and robot control. Despite its potential, traditional RL is extremely sample inefficient. Learning a task that a human could pick up in a few tries can take an agent… Read More »A New AI Research Proposes ‘First-Explore’: A Simple AI Framework For Meta-RL With Two Policies That Is One Policy Learns To Only Explore And One Policy Learns To Only Exploit Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost
Recent advancements and releases in the field of Artificial Intelligence have been able to bring a wave of storm in the community. From researchers to students, everyone’s inculcating the applications of AI to solve tasks in everyday life. With the release of OpenAI’s chatbot,… Read More »H2O.ai Introduces h2oGPT: A Suite of Open-Source Code Repositories for Democratizing Large Language Models (LLMs) Tanya Malhotra Artificial Intelligence Category – MarkTechPost
Language models have made significant strides in natural language processing tasks. However, deploying large language models (LLMs) in real-world applications requires addressing their deficit in moral reasoning capabilities. To tackle this challenge, a Google research team introduces a groundbreaking framework called “Thought Experiments,” which… Read More »Google AI Proposes ‘Thought Experiments’ to Enhance Moral Reasoning in Language Models Niharika Singh Artificial Intelligence Category – MarkTechPost