ImageBind is a model by Meta AI which can make sense out of six different types of data. This is exciting because it brings AI a step closer to how humans are observing the environment using multiple senses. In this post, we will explain what is this model, why should we care about it by […]
ImageBind: One Embedding Space To Bind Them All Read More »
Consistency models are a new type of generative models which were introduced by Open AI in a paper titled Consistency Models.In this post we will discuss about why consistency models are interesting, what they are and how they are created.Let’s start by asking why should we care about consistency models? If you prefer a video
Consistency Models – Optimizing Diffusion Models Inference Read More »
LIMA, Less Is More for Alignment , is a LLM which was created by Meta AI by fine-tuning the LLaMa model on only 1000 samples, and achieved competitive results with top large language models such as GPT-4, Bard and Alpaca. In this post, our goal is to explain the research paper by discussing what the
LIMA from Meta AI – Less Is More for Alignment of LLMs Read More »
In this post we will explore Shepherd, a new research paper by Meta AI titled Shepherd: A Critic for Language Model Generation. Large language models have become increasingly sophisticated and given a prompt are able to generate a correct, beautiful and meaningful text. However, a remaining concern with large language models is their tendency to
Shepherd: A Critic for Language Model Generation Read More »
Foundational large language models such as GPT-4, GPT-3.5, LLaMA and more are trained on huge corpus of text from the internet, which contains a large amount of offensive information. Therefore, these foundational large language models are capable of generating a great deal of objectionable content. For this reason, there are a lot of efforts to
Universal and Transferable Adversarial LLM Attacks Read More »
In this post we dive into Meta-Transformer, a multimodal learning method, which was presented in a research paper titled Meta-Transformer: A Unified Framework for Multimodal Learning. In the paper, the researchers show they were able to process information from 12(!) different modalities that we see in the picture above, which includes image, text, audio, infrared,
Meta-Transformer: A Unified Framework for Multimodal Learning Read More »
In this post we will dive into a research paper by Google DeepMind titled “From Sparse to Soft Mixtures of Experts”. In recent years we see that transformer-based models are getting larger and larger in order to improve their performance. An undesirable consequence is that the computational cost is also getting larger. And here comes
From Sparse to Soft Mixture of Experts Read More »
In this post we dive into YOLO-NAS, an improved version in the YOLO models family for object detection which was precented earlier this year by Deci. YOLO models have been around for a while now, presented in 2015 with the paper You Only Look Once, which is what the shortcut YOLO stands for, and over
What is YOLO-NAS and How it Was Created Read More »
