3,080 technical terms and definitions
Reflexion, Shinn 2023, Self-Refine, Madaan 2023, intrinsic self-correction, Huang 2023, self-critique
Self-reflection, in the language-model sense, is the practice of having a model examine its own output (an answer, a plan, a piece of code, a failed attempt) produce a critique…
Mixup-Induced Consistency Regularization for SSL Introduction & Motivation Mixup-Induced Consistency Regularization for SSL addresses a central problem in semisupervised…
3d vision
Self-supervised depth and ego-motion learning is the joint training paradigm where depth and camera pose networks are optimized using view synthesis consistency instead…
representation learning
Self-Supervised Disentanglement is the representation learning paradigm that learns factorized latent representations without labels, where each latent dimension corresponds to…
Contrastive Learning for 12-Lead Electrocardiograms Introduction & Motivation Contrastive Learning for 12-Lead Electrocardiograms addresses a central problem in selfsupervised…
Self-Supervised Pretraining with Meta-Metric Fine-Tuning Introduction & Motivation Self-Supervised Pretraining with Meta-Metric Fine-Tuning addresses a central problem in fewshot…
Self-Supervised Few-Shot Introduction & Motivation Self-Supervised Few-Shot addresses a central problem in fewshot and pretraining-via-self-supervised architecture: how…
Self-Supervised Transfer Effectiveness Introduction & Motivation Self-Supervised Transfer Effectiveness addresses a central problem in selfsupervised…
SatMAE Masked Autoencoders for Earth Observation Introduction & Motivation SatMAE Masked Autoencoders for Earth Observation addresses a central problem in selfsupervised…
graph neural networks
Self-Supervised GNN is graph representation learning without manual labels using pretext or contrastive objectives - It enables scalable pretraining from structure and feature…
Contrastive Deep Graph Infomax for Community Mining Introduction & Motivation Contrastive Deep Graph Infomax for Community Mining addresses a central problem in selfsupervised…
Self-Supervised Graph Learning SGL for RecSys Introduction & Motivation Self-Supervised Graph Learning SGL for RecSys addresses a central problem in recsys systems and {target}…
self-supervised learning, contrastive learning, masked prediction, foundation pretraining
Self-supervised learning learns representations from supervision constructed from the data itself rather than manually assigned task labels. It unlocks web-scale text, images…
101248 self supervised learning, 101248 self, self supervised, byol cifar, embedding layers & word representations, embedding layers word, layers word representations, embedding layers, layers word
Embedding Layers & Word Representations Introduction & Motivation Embeddings: map discrete to continuous. Word2Vec, GloVe, FastText. Applications: NLP, language understanding.…
pretext tasks, contrastive learning, representation learning, unsupervised pretraining
Self-Supervised Learning and Pretext Tasks — Learning Representations Without Labels Self-supervised learning (SSL) has revolutionized deep learning by enabling models to learn…
self supervised learning contrastive methods learning from unlabeled data, self supervised learning contrastive methods learning unlabeled data, self supervised
Self-Supervised Learning & Contrastive Methods: Learning from Unlabeled Data 1. Introduction & Motivation Self-supervised learning (SSL) represents a paradigm shift: learning…
self-supervised learning contrastive prediction, self-supervised learning contrastive, learning contrastive prediction, self-supervised learning, learning contrastive
Self-Supervised Learning Contrastive Prediction with Machine Learning Introduction & Motivation Self-Supervised Learning Contrastive Prediction with Machine Learning addresses…
self supervised learning contrastive representation learning, self supervised, learning contrastive representation, 2n byol gpu nt passed, self-supervised learning contrastive
Self-Supervised Learning: Contrastive & Representation Learning Introduction & Motivation Self-Supervised Learning: learn from unlabeled data. Contrastive Learning: maximize…
data analysis
Self-Supervised Learning (SSL) for Anomaly Detection is the training of models on only normal (defect-free) data using self-supervised tasks — the model learns the distribution…
self supervised learning methods, self supervised, supervised learning, self-supervised learning, learning methods
Self-Supervised Learning Methods Introduction & Motivation Self-Supervised Learning Methods addresses a central problem in representation learning from unlabeled data: how…
self supervised learning pretext tasks representation learning, self supervised, learning pretext tasks, 1m bert byol cifar gpt nlp, self-supervised learning pretext
Self-Supervised Learning: Pretext Tasks & Representation Learning Introduction & Motivation Self-supervised learning: learn representations from unlabeled data via pretext tasks.…
self supervised learning vision, self supervised learning, supervised learning vision, self supervised, supervised learning, learning vision
Self Supervised Learning Vision Introduction & Motivation Self Supervised Learning Vision addresses a central problem in vision process control…
contrastive pretraining image, dino self supervised, mae masked autoencoder, pretext task representation
Self-Supervised Visual Learning is the training paradigm that learns powerful visual representations from unlabeled images by solving pretext tasks (predicting masked patches…
Masked Autoencoding with Positional Perturbation Anomaly Introduction & Motivation Masked Autoencoding with Positional Perturbation Anomaly addresses a central problem in anomaly…
3d vision
Self-supervised monocular depth estimation is the training approach that learns single-image depth prediction from unlabeled stereo pairs or monocular video using photometric…
Point-MAE Masked Autoencoders for 3D Point Clouds Introduction & Motivation Point-MAE Masked Autoencoders for 3D Point Clouds addresses a central problem in selfsupervised…
computer vision
Self-supervised pre-training for ViT is the approach of learning strong visual representations from unlabeled images through reconstruction, contrastive, or distillation…
self-supervised pretraining
Self-Supervised Pretraining Introduction & Motivation Self-Supervised Pretraining addresses a central problem in ml-training process control and downstream-accuracy-improvement…
self supervised pretraining, self supervised, supervised pretraining
Self Supervised Pretraining Introduction & Motivation Self Supervised Pretraining addresses a central problem in timeseries process control and self-supervised-learning-unlabeled…
Self-Supervised Vision Pretraining Introduction & Motivation Self-Supervised Vision Pretraining addresses a central problem in vision processing and pretraining-effectiveness…
CURL Contrastive Unsupervised Representations for RL Introduction & Motivation CURL Contrastive Unsupervised Representations for RL addresses a central problem in selfsupervised…
Self-Predictive Representations SPR for Deep RL Introduction & Motivation Self-Predictive Representations SPR for Deep RL addresses a central problem in selfsupervised…
HuBERT Hidden-Unit BERT for Self-Supervised Speech Introduction & Motivation HuBERT Hidden-Unit BERT for Self-Supervised Speech addresses a central problem in selfsupervised…
wav2vec hubert whisper, speech representation learning, audio foundation models, speech pretraining
Self-Supervised Speech Models are foundation models pretrained on large corpora of unlabeled audio that learn general-purpose speech representations through contrastive…
SAINT Self-Attention and Intersample Contrastive for Tabular Introduction & Motivation SAINT Self-Attention and Intersample Contrastive for Tabular addresses a central problem…
Trajectory Contrastive Learning for Autonomous Driving Introduction & Motivation Trajectory Contrastive Learning for Autonomous Driving addresses a central problem…
Contrastive Video Predictive Coding for Unsupervised Actions Introduction & Motivation Contrastive Video Predictive Coding for Unsupervised Actions addresses a central problem…
Contrastive Learning for Photoplethysmography PPG Signals Introduction & Motivation Contrastive Learning for Photoplethysmography PPG Signals addresses a central problem…
Self-Taught Learning via Sparse Dictionary Coding Introduction & Motivation Self-Taught Learning via Sparse Dictionary Coding addresses a central problem in transfer learning…
design
Self-timed asynchronous circuits replace the global clock with local handshaking protocols, making them inherently robust to process variation and voltage fluctuation. Instead…
semi-supervised learning
Self-training uses a model's own predictions on unlabeled data as training labels for semi-supervised learning. Process: Train on labeled data → predict on unlabeled data → select…
advanced training
Self-training is a semi-supervised approach where a model generates labels for unlabeled data and retrains on confident predictions - Pseudo-labeled samples expand training…
pseudo labeling, semi supervised, noisy student, teacher student self training
Self-Training (Pseudo-Labeling) is the semi-supervised learning technique where a model trained on labeled data generates predictions (pseudo-labels) on unlabeled data, then…
Self-Training Domain Adaptation Introduction & Motivation Self-Training Domain Adaptation addresses a central problem in transfer learning and {target} adaptation: how to Apply…
Self-Training Iterative Method Introduction & Motivation Self-Training Iterative Method addresses a central problem in semisupervised and model-improvement-iteration architecture…
Noisy Student Training with Iterative Teacher-Student Loops Introduction & Motivation Noisy Student Training with Iterative Teacher-Student Loops addresses a central problem…
reasoning
Self-verification is the technique of having a language model check, critique, and validate its own generated answers — using a separate inference pass (or structured…
selfies, chemistry ai
SELFIES (Self-Referencing Embedded Strings) is a molecular string representation designed to guarantee that every possible string corresponds to a valid molecular graph…
selu, neural architecture
SELU (Scaled Exponential Linear Unit) is a self-normalizing activation function that automatically maintains zero mean and unit variance of activations — with specific scale…
sem cd measurement accuracy drift, sem cd measurement, cd measurement accuracy, measurement accuracy drift, sem cd, cd measurement, measurement accuracy, accuracy drift
SEM CD Measurement Accuracy Drift with Machine Learning Introduction & Motivation SEM CD Measurement Accuracy Drift with Machine Learning addresses a central problem in scanning…