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Large text-to-video models trained on internet-scale data have shown extraordinary capabilities to generate high-fidelity films from arbitrarily written descriptions. However, fine-tuning a pretrained huge model might be prohibitively expensive, making it difficult to adapt these models...
Researchers have proposed a novel approach to enforcing distributional constraints in machine learning models using multi-marginal optimal transport. This approach is designed to be computationally efficient and allows for efficient computation of gradients during backpropagation. Existing methods...

Apple Researchers Introduce ByteFormer: An AI Model That Consumes Only Bytes And Does Not Explicitly Model The Input Modality

The explicit modeling of the input modality is typically required for deep learning inference. For instance, by encoding picture patches into vectors, Vision Transformers...

MIT Researchers Propose A New Multimodal Technique That Blends Machine Learning Methods To Learn More Similarly To Humans

Artificial intelligence is revolutionary in all the major use cases and applications we encounter daily. One such area revolves around a lot of audio...

Meet SpQR (Sparse-Quantized Representation): A Compressed Format And Quantization Technique That Enables Near-Lossless Large Language Model Weight Compression

Large Language Models (LLMs) have demonstrated incredible capabilities in recent times. Learning from massive amounts of data, these models have been performing tasks with...

A New AI Research Introduces A Novel Enhanced Prompting Framework for Text Generation

The natural language creation field is completely transformed by large language models (LLMs). Traditional fine-tuning approaches for responding to downstream tasks require access to...

Meet PRODIGY: A Pretraining AI Framework That Enables In-Context Learning Over Graphs

The GPT model, which is the transformer architecture behind the well famous chatbot developed by OpenAI called ChatGPT, works on the concept of learning...

CMU Researchers Introduce ReLM: An AI System For Validating And Querying LLMs Using Standard Regular Expressions

There are rising worries about the potential negative impacts of large language models (LLMs), such as data memorization, bias, and unsuitable language, despite LLMs'...

Google Researchers Introduce StyleDrop: An AI Method that Enables the Synthesis of Images that Faithfully Follow a Specific Style Using a Text-to-Image Model

A group of researchers from Google have recently unveiled StyleDrop, an innovative neural network developed in collaboration with Muse's fast text-to-image model. This groundbreaking...

ETH Zurich and HKUST Researchers Propose HQ-SAM: A High-Quality Zero-Shot Segmentation Model By Introducing Negligible Overhead To The Original SAM

Accurate segmentation of multiple objects is essential for various scene understanding applications, such as image/video processing, robotic perception, and AR/VR. The Segment Anything Model...

Meet Pix2Act: An AI Agent That Can Interact With GUIs Using The Same Conceptual Interface That Humans Commonly Use Via Pixel-Based Screenshots And Generic...

By enabling users to connect with tools and services, systems that can follow directions from graphical user interfaces (GUIs) can automate laborious jobs, increase...

Discovering the Apple Vision Pro: 6 Mind-Blowing Hidden Features to Explore

Apple has announced the release of Apple Vision Pro, a groundbreaking spatial computer that seamlessly integrates digital content with the physical world. This innovative...

Stanford Researchers Introduce CWM (Counterfactual World Modeling): A Framework That Unifies Machine Vision

In recent times, there has been significant progress in Natural Language Understanding and Natural Language Generation. The best example is the well-known ChatGPT developed...

Scaling Generative Retrieval: Google Research and University of Waterloo’s Empirical Study on Generative Retrieval Across Diverse Corpus Scales, Including a Deep Dive into the...

In a revolutionary leap forward, generative retrieval approaches have emerged as a disruptive paradigm in information retrieval methods. Harnessing the potential of advanced sequence-to-sequence...

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