Textual Inversion
Textual Inversion is a technique for personalizing text-to-image diffusion models that teaches a frozen model a new visual concept from only three to five example images by learning a single new "pseudo-word"…
Deep Learning
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Showing 301-333 of 333 articles
Time Series
A time series is a sequence of observations recorded in time order, often at regular intervals: daily retail sales, hourly electricity demand, per-minute server latency, quarterly GDP.
Token
A token is the basic unit of text that a language model reads and writes: a word, a subword fragment, a single character, or a byte, produced by splitting text during a step called tokenization.
Training
Training in machine learning is the process of fitting a model's parameters to data so that the model can make accurate predictions or generate useful outputs.
Transfer Learning
Transfer learning is a family of machine learning methods in which information learned from one source domain or task is used while learning a target domain or task.
Transformers
A Transformer is a deep learning architecture, introduced by eight Google researchers in the 2017 paper "Attention Is All You Need", that uses attention as the sole mechanism for modeling relationships between…
Transfusion
Transfusion is a recipe, introduced by Meta in 2024, for training a single Transformer over a mixture of discrete text and continuous image data using two training objectives simultaneously: a…
Translational invariance
Translational invariance (also called translation invariance or shift invariance) is the property of a function, system, or machine learning model whose output does not change when its input is translated…
Twin Delayed DDPG
Twin Delayed Deep Deterministic Policy Gradient (TD3) is an off-policy actor-critic reinforcement learning algorithm for continuous action spaces, introduced by Scott Fujimoto, Herke van Hoof, and David Meger…
U-Net
U-Net is a convolutional neural network architecture designed for biomedical image segmentation.
VGG
VGG (also called VGGNet) is a deep convolutional neural network architecture, introduced in 2014 by Karen Simonyan and Andrew Zisserman of the Visual Geometry Group at the University of Oxford
VQ-VAE (Vector Quantized Variational Autoencoder)
VQ-VAE (Vector Quantized Variational Autoencoder) is a generative neural network that compresses data into a grid or sequence of discrete tokens drawn from a learned codebook
VQGAN (Taming Transformers)
VQGAN (Vector Quantized Generative Adversarial Network) is a two-stage image-synthesis method that first compresses an image into a small grid of discrete codebook tokens with an adversarially trained…
Vanishing Gradient Problem
The vanishing gradient problem is a difficulty in training deep neural networks where the gradients used to update the network shrink exponentially as they are propagated backward through the layers, leaving…
Variational Autoencoder
A variational autoencoder (VAE) is a latent-variable generative model that pairs a probabilistic decoder with a learned approximation to posterior inference.
Veo
Veo is a family of text-to-video generative AI models developed by Google DeepMind, and is best known as the first video model from a leading AI lab to natively generate synchronized audio (dialogue, sound…
Vision Encoder
A vision encoder is the neural network component that turns an image into a sequence of numerical vectors that other models can consume.
Visual Autoregressive modeling (VAR)
Visual Autoregressive modeling (VAR) is an image generation paradigm, introduced in 2024, that reframes autoregressive image synthesis as coarse-to-fine "next-scale prediction" rather than the conventional…
Wasserstein GAN (WGAN)
A Wasserstein GAN (WGAN) is a generative adversarial network that trains its two networks to minimise the Wasserstein-1 distance, also called the Earth mover's distance, between the real data distribution and…
Wav2Vec
Wav2Vec is a family of self-supervised learning models from Meta AI (formerly Facebook AI Research) that learn speech representations directly from raw audio waveforms
WaveNet
WaveNet is a deep generative model for raw audio waveforms developed by DeepMind that synthesizes speech by predicting one waveform sample at a time, each conditioned on all the samples before it.
Whisper
Whisper is an open-source family of automatic speech recognition (ASR) models developed by OpenAI and first released on September 21, 2022.
Word Embedding
A word embedding is a learned representation of text in which words are mapped to dense vectors of real numbers in a continuous vector space, so that words with similar meanings are positioned close together.
XLNet
XLNet is a generalized autoregressive pretraining method for natural language processing that combines the strengths of autoregressive and autoencoding language models.
YOLO (object detection)
YOLO (You Only Look Once) is a family of object detection models that treat detection as a single regression problem, predicting bounding boxes and class probabilities directly from full images in one forward…
YOLOv8
YOLOv8 is a family of one-stage computer vision models released by Ultralytics on January 10, 2023.
YaRN
YaRN (Yet another RoPE extensioN) is a compute-efficient method for extending the context window of large language models that use Rotary Position Embeddings (RoPE).
ZeRO (Zero Redundancy Optimizer)
ZeRO (Zero Redundancy Optimizer) is a family of memory-optimization techniques for training large neural networks introduced by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He at microsoft…
muP (Maximal Update Parametrization)
muP, short for Maximal Update Parametrization (often written muP, μP, or mu-P), is a parametrization scheme for deep neural networks in which a small set of optimization hyperparameters, most importantly the…
tf.keras
tf.keras is the high-level deep learning API built directly into the TensorFlow machine learning framework
tinygrad
tinygrad is an open-source deep learning framework written primarily in Python that aims to occupy the space between Andrej Karpathy's pedagogical micrograd and full-scale production stacks like PyTorch.
torch.compile
torch.compile is the just-in-time graph capture and compilation feature introduced in PyTorch 2.0, a release first announced at the PyTorch Conference on December 2, 2022 and shipped as a stable version on…
xLSTM
xLSTM (Extended Long Short-Term Memory) is a recurrent neural network architecture introduced in May 2024 by Maximilian Beck, Korbinian Pöppel, Sepp Hochreiter, and collaborators at Johannes Kepler University…