From 58f89b1302baad6217497a4030497450ecad8ae0 Mon Sep 17 00:00:00 2001 From: "Rifqi D. Panuluh" Date: Mon, 3 Nov 2025 15:53:47 +0700 Subject: [PATCH] feat(latex): add common AI/ML acronyms with proper names and improve formatting --- latex/frontmatter/acronym.tex | 379 +++++++++++++++++++++++++++++++--- 1 file changed, 346 insertions(+), 33 deletions(-) diff --git a/latex/frontmatter/acronym.tex b/latex/frontmatter/acronym.tex index 1f2faa6..0f9f39a 100644 --- a/latex/frontmatter/acronym.tex +++ b/latex/frontmatter/acronym.tex @@ -1,48 +1,361 @@ % Define an abbreviation (acronym) -% Acronyms for the thesis -\newacronym{ml}{ML}{machine learning} -\newacronym{stft}{STFT}{short-time fourier transform} -\newacronym{ai}{AI}{artificial intelligence} -\newacronym{dl}{DL}{deep learning} -\newacronym{nn}{NN}{neural network} -\newacronym{fft}{FFT}{fast fourier transform} -\newacronym{svm}{SVM}{support vector machine} -\newacronym{cnn}{CNN}{convolutional neural network} -\newacronym{rnn}{RNN}{recurrent neural network} -\newacronym{vbi}{VBI}{vibration-based inspection} -\newacronym{shm}{SHM}{structural health monitoring} -\newacronym{fea}{FEA}{finite element analysis} -\newacronym{1d-cnn}{1-D CNN}{\textit{One-Dimensional Convolutional Neural Network}} +% Acronyms for indonesian thesis +\newacronym{ml}{ML}{\textit{machine learning}} +\newacronym{stft}{STFT}{\textit{short-time} Fourier \textit{transform}} +\newacronym{ai}{AI}{\textit{artificial intelligence}} +\newacronym{dl}{DL}{\textit{deep learning}} +\newacronym{nn}{NN}{\textit{neural network}} +\newacronym{fft}{FFT}{\textit{fast} Fourier \textit{transform}} +\newacronym{svm}{SVM}{\textit{support vector machine}} +\newacronym{cnn}{CNN}{\textit{convolutional neural network}} +\newacronym{rnn}{RNN}{\textit{recurrent neural network}} +\newacronym{vbi}{VBI}{\textit{vibration-based inspection}} +\newacronym{shm}{SHM}{\textit{structural health monitoring}} +\newacronym{fea}{FEA}{\textit{finite element analysis}} +\newacronym{1d-cnn}{1-D CNN}{\textit{one-dimensional convolutional neural network}} \newacronym{pca}{PCA}{\textit{principal component analysis}} -% rbf \newacronym{rbf}{RBF}{\textit{radial basis function}} -% cm \newacronym{cm}{CM}{\textit{confusion matrix}} -\newacronym{tsne}{t-SNE}{\textit{t-distributed stochastic neighbor embedding}} +\newacronym{tsne}{t-SNE}{\textit{t-Distributed Stochastic Neighbor Embedding}} \newacronym{pacmap}{PaCMAP}{\textit{Pairwise Controlled Manifold Approximation Projection}} -% AR \newacronym{ar}{AR}{\textit{autoregressive}} -% residual error \newacronym{re}{RE}{\textit{residual error}} -% ae \newacronym{ae}{AE}{\textit{autoencoder}} -% lr \newacronym{lr}{LR}{\textit{logistic regression}} -% ann \newacronym{ann}{ANN}{\textit{artificial neural network}} -% wt \newacronym{wt}{WT}{\textit{wavelet transform}} -% oc-svm \newacronym{oc-svm}{OC-SVM}{\textit{one-class support vector machine}} -% lstm \newacronym{lstm}{LSTM}{\textit{long short-term memory}} -% ga \newacronym{ga}{GA}{\textit{genetic algorithm}} -% ht -\newacronym{ht}{HT}{\textit{Hilbert Transform}} -% vmd -\newacronym{vmd}{VMD}{\textit{Variational Mode Decomposition}} -% qugs +\newacronym{ht}{HT}{Hilbert \textit{transform}} +\newacronym{vmd}{VMD}{\textit{variational mode decomposition}} \newacronym{qugs}{QUGS}{\textit{Qatar University Grandstand Simulator}} -% rcnn -\newacronym{r-cnn}{R-CNN}{\textit{Region-based Convolutional Neural Network}} \ No newline at end of file +\newacronym{r-cnn}{R-CNN}{\textit{region-based convolutional neural network}} +\newacronym{pso}{PSO}{\textit{particle swarm optimization}} + +% ADDED - Common AI/ML acronyms with proper names +\newacronym{hmm}{HMM}{\textit{hidden} Markov \textit{model}} +\newacronym{gru}{GRU}{\textit{gated recurrent unit}} +\newacronym{gan}{GAN}{\textit{generative adversarial network}} +\newacronym{vae}{VAE}{\textit{variational autoencoder}} +\newacronym{adam}{Adam}{Adam} % optimizer named after "adaptive moment estimation" +\newacronym{sgd}{SGD}{\textit{stochastic gradient descent}} +\newacronym{relu}{ReLU}{\textit{rectified linear unit}} +\newacronym{elu}{ELU}{\textit{exponential linear unit}} +\newacronym{selu}{SELU}{\textit{scaled exponential linear unit}} +\newacronym{gelu}{GELU}{Gaussian \textit{error linear unit}} +\newacronym{bert}{BERT}{\textit{Bidirectional Encoder Representations from Transformers}} +\newacronym{gpt}{GPT}{\textit{Generative Pre-trained Transformer}} +\newacronym{resnet}{ResNet}{\textit{Residual Network}} +\newacronym{yolo}{YOLO}{\textit{You Only Look Once}} +\newacronym{fcn}{FCN}{\textit{fully convolutional network}} +\newacronym{rlhf}{RLHF}{\textit{reinforcement learning from human feedback}} +\newacronym{ppo}{PPO}{\textit{proximal policy optimization}} +\newacronym{dqn}{DQN}{\textit{deep Q-network}} +\newacronym{ddpg}{DDPG}{\textit{deep deterministic policy gradient}} +\newacronym{a3c}{A3C}{\textit{asynchronous advantage actor-critic}} +\newacronym{sac}{SAC}{\textit{soft actor-critic}} +\newacronym{td}{TD}{\textit{temporal difference}} +\newacronym{mdp}{MDP}{Markov \textit{decision process}} +\newacronym{pomdp}{POMDP}{\textit{partially observable} Markov \textit{decision process}} +\newacronym{mcmc}{MCMC}{Markov \textit{chain} Monte Carlo} +\newacronym{em}{EM}{\textit{expectation-maximization}} +\newacronym{map}{MAP}{\textit{maximum a posteriori}} +\newacronym{mle}{MLE}{\textit{maximum likelihood estimation}} +\newacronym{kl}{KL}{Kullback-Leibler} +\newacronym{js}{JS}{Jensen-Shannon} +\newacronym{knn}{k-NN}{\textit{k-nearest neighbors}} +\newacronym{dbscan}{DBSCAN}{\textit{density-based spatial clustering of applications with noise}} +\newacronym{kmeans}{k-means}{\textit{k-means}} +\newacronym{gmm}{GMM}{Gaussian \textit{mixture model}} +\newacronym{gp}{GP}{Gaussian \textit{process}} +\newacronym{gpr}{GPR}{Gaussian \textit{process regression}} +\newacronym{bo}{BO}{Bayesian \textit{optimization}} +\newacronym{tpe}{TPE}{\textit{tree-structured} Parzen \textit{estimator}} +\newacronym{smbo}{SMBO}{\textit{sequential model-based optimization}} +\newacronym{nas}{NAS}{\textit{neural architecture search}} +\newacronym{automl}{AutoML}{\textit{automated machine learning}} +\newacronym{xgboost}{XGBoost}{\textit{eXtreme Gradient Boosting}} +\newacronym{gbdt}{GBDT}{\textit{gradient boosting decision tree}} +\newacronym{rf}{RF}{\textit{random forest}} +\newacronym{dt}{DT}{\textit{decision tree}} +\newacronym{cart}{CART}{\textit{classification and regression tree}} +\newacronym{id3}{ID3}{\textit{Iterative Dichotomiser 3}} +\newacronym{c45}{C4.5}{C4.5} +\newacronym{adaboost}{AdaBoost}{\textit{adaptive boosting}} +\newacronym{lasso}{LASSO}{\textit{least absolute shrinkage and selection operator}} +\newacronym{ridge}{Ridge}{Ridge \textit{regression}} +\newacronym{enet}{ElasticNet}{ElasticNet} +\newacronym{svr}{SVR}{\textit{support vector regression}} +\newacronym{kpca}{KPCA}{\textit{kernel principal component analysis}} +\newacronym{lda}{LDA}{\textit{linear discriminant analysis}} +\newacronym{ica}{ICA}{\textit{independent component analysis}} +\newacronym{nmf}{NMF}{\textit{non-negative matrix factorization}} +\newacronym{umap}{UMAP}{\textit{Uniform Manifold Approximation and Projection}} +\newacronym{isomap}{Isomap}{\textit{Isometric Mapping}} +\newacronym{lle}{LLE}{\textit{locally linear embedding}} +\newacronym{mds}{MDS}{\textit{multidimensional scaling}} +\newacronym{som}{SOM}{\textit{self-organizing map}} +\newacronym{bpnn}{BPNN}{\textit{backpropagation neural network}} +\newacronym{elm}{ELM}{\textit{extreme learning machine}} +\newacronym{sae}{SAE}{\textit{stacked autoencoder}} +\newacronym{dae}{DAE}{\textit{denoising autoencoder}} +\newacronym{vq-vae}{VQ-VAE}{\textit{vector quantized variational autoencoder}} +\newacronym{wgan}{WGAN}{Wasserstein GAN} +\newacronym{dcgan}{DCGAN}{\textit{deep convolutional generative adversarial network}} +\newacronym{cgan}{cGAN}{\textit{conditional generative adversarial network}} +\newacronym{cyclegan}{CycleGAN}{CycleGAN} +\newacronym{pix2pix}{Pix2Pix}{Pix2Pix} +\newacronym{stylegan}{StyleGAN}{StyleGAN} +\newacronym{clip}{CLIP}{\textit{Contrastive Language-Image Pre-training}} +\newacronym{vit}{ViT}{\textit{Vision Transformer}} +\newacronym{swin}{Swin}{\textit{Shifted Window Transformer}} +\newacronym{detr}{DETR}{\textit{DEtection TRansformer}} +\newacronym{rcnn}{R-CNN}{\textit{region-based convolutional neural network}} +\newacronym{fast-rcnn}{Fast R-CNN}{Fast R-CNN} +\newacronym{faster-rcnn}{Faster R-CNN}{Faster R-CNN} +\newacronym{mask-rcnn}{Mask R-CNN}{Mask R-CNN} +\newacronym{fpn}{FPN}{\textit{feature pyramid network}} +\newacronym{ssd}{SSD}{\textit{single shot detector}} +\newacronym{seresnet}{SE-ResNet}{\textit{Squeeze-and-Excitation ResNet}} +\newacronym{nlp}{NLP}{\textit{natural language processing}} +\newacronym{nlu}{NLU}{\textit{natural language understanding}} +\newacronym{nlg}{NLG}{\textit{natural language generation}} +\newacronym{seq2seq}{Seq2Seq}{\textit{sequence-to-sequence}} +\newacronym{bleu}{BLEU}{\textit{Bilingual Evaluation Understudy}} +\newacronym{rouge}{ROUGE}{\textit{Recall-Oriented Understudy for Gisting Evaluation}} +\newacronym{meteor}{METEOR}{\textit{Metric for Evaluation of Translation with Explicit ORdering}} +\newacronym{cider}{CIDEr}{\textit{Consensus-based Image Description Evaluation}} +\newacronym{wer}{WER}{\textit{word error rate}} +\newacronym{cer}{CER}{\textit{character error rate}} +\newacronym{perplexity}{PPL}{\textit{perplexity}} +\newacronym{bpe}{BPE}{\textit{byte pair encoding}} +\newacronym{wordpiece}{WordPiece}{WordPiece} +\newacronym{sentencepiece}{SentencePiece}{SentencePiece} +\newacronym{tfidf}{TF-IDF}{\textit{term frequency-inverse document frequency}} +\newacronym{bow}{BoW}{\textit{bag of words}} +\newacronym{cbow}{CBOW}{\textit{continuous bag of words}} +\newacronym{skipgram}{Skip-gram}{Skip-gram} +\newacronym{word2vec}{Word2Vec}{Word2Vec} +\newacronym{glove}{GloVe}{\textit{Global Vectors for Word Representation}} +\newacronym{fasttext}{FastText}{FastText} +\newacronym{elmo}{ELMo}{\textit{Embeddings from Language Models}} +\newacronym{ulmfit}{ULMFiT}{\textit{Universal Language Model Fine-tuning}} +\newacronym{t5}{T5}{\textit{Text-to-Text Transfer Transformer}} +\newacronym{bart}{BART}{\textit{Bidirectional and Auto-Regressive Transformers}} +\newacronym{electra}{ELECTRA}{\textit{Efficiently Learning an Encoder that Classifies Token Replacements Accurately}} +\newacronym{roberta}{RoBERTa}{\textit{Robustly Optimized BERT Pretraining Approach}} +\newacronym{ernie}{ERNIE}{\textit{Enhanced Representation through kNowledge IntEgration}} +\newacronym{flash-attention}{FlashAttention}{FlashAttention} +\newacronym{mha}{MHA}{\textit{multi-head attention}} +\newacronym{mqa}{MQA}{\textit{multi-query attention}} +\newacronym{gqa}{GQA}{\textit{grouped-query attention}} +\newacronym{kv-cache}{KV-cache}{\textit{key-value cache}} +\newacronym{rag}{RAG}{\textit{retrieval-augmented generation}} +\newacronym{dpr}{DPR}{\textit{dense passage retrieval}} +\newacronym{faiss}{FAISS}{\textit{Facebook AI Similarity Search}} +\newacronym{annoy}{Annoy}{\textit{Approximate Nearest Neighbors Oh Yeah}} +\newacronym{hnsw}{HNSW}{\textit{Hierarchical Navigable Small World}} +\newacronym{ivf}{IVF}{\textit{inverted file index}} +\newacronym{pq}{PQ}{\textit{product quantization}} +\newacronym{lsh}{LSH}{\textit{locality-sensitive hashing}} +\newacronym{mips}{MIPS}{\textit{maximum inner product search}} +\newacronym{rl}{RL}{\textit{reinforcement learning}} +\newacronym{drl}{DRL}{\textit{deep reinforcement learning}} +\newacronym{irl}{IRL}{\textit{inverse reinforcement learning}} +\newacronym{gail}{GAIL}{\textit{generative adversarial imitation learning}} +\newacronym{il}{IL}{\textit{imitation learning}} +\newacronym{bc}{BC}{\textit{behavioral cloning}} +\newacronym{marl}{MARL}{\textit{multi-agent reinforcement learning}} +\newacronym{maddpg}{MADDPG}{\textit{multi-agent deep deterministic policy gradient}} +\newacronym{vdn}{VDN}{\textit{value decomposition network}} +\newacronym{coma}{COMA}{\textit{counterfactual multi-agent policy gradient}} +\newacronym{mappo}{MAPPO}{\textit{multi-agent proximal policy optimization}} +\newacronym{trpo}{TRPO}{\textit{trust region policy optimization}} +\newacronym{td3}{TD3}{\textit{twin delayed deep deterministic policy gradient}} +\newacronym{her}{HER}{\textit{hindsight experience replay}} +\newacronym{per}{PER}{\textit{prioritized experience replay}} +\newacronym{c51}{C51}{\textit{categorical 51}} +\newacronym{qr-dqn}{QR-DQN}{\textit{quantile regression DQN}} +\newacronym{iqn}{IQN}{\textit{implicit quantile network}} +\newacronym{icm}{ICM}{\textit{intrinsic curiosity module}} +\newacronym{rnd}{RND}{\textit{random network distillation}} +\newacronym{ngu}{NGU}{\textit{Never Give Up}} +\newacronym{curl}{CURL}{\textit{Contrastive Unsupervised Representations for Reinforcement Learning}} +\newacronym{mcts}{MCTS}{Monte Carlo \textit{tree search}} +\newacronym{uct}{UCT}{\textit{upper confidence bound applied to trees}} +\newacronym{puct}{PUCT}{\textit{predictor + upper confidence bound applied to trees}} +\newacronym{ucb}{UCB}{\textit{upper confidence bound}} +\newacronym{ts}{TS}{Thompson \textit{sampling}} +\newacronym{mab}{MAB}{\textit{multi-armed bandit}} +\newacronym{cmab}{CMAB}{\textit{contextual multi-armed bandit}} +\newacronym{exp3}{EXP3}{\textit{exponential-weight algorithm for exploration and exploitation}} +\newacronym{linucb}{LinUCB}{\textit{linear upper confidence bound}} +\newacronym{scaffold}{SCAFFOLD}{\textit{Stochastic Controlled Averaging for Federated Learning}} +\newacronym{fedbn}{FedBN}{\textit{Federated learning with Batch Normalization}} +\newacronym{moon}{MOON}{\textit{Model-Contrastive Federated Learning}} +\newacronym{feddf}{FedDF}{\textit{Federated Learning via Knowledge Distillation with Dynamic Regularization}} +\newacronym{fl}{FL}{\textit{federated learning}} +\newacronym{dp}{DP}{\textit{differential privacy}} +\newacronym{ldp}{LDP}{\textit{local differential privacy}} +\newacronym{pate}{PATE}{\textit{Private Aggregation of Teacher Ensembles}} +\newacronym{smpc}{SMPC}{\textit{secure multi-party computation}} +\newacronym{he}{HE}{\textit{homomorphic encryption}} +\newacronym{phe}{PHE}{\textit{partial homomorphic encryption}} +\newacronym{fhe}{FHE}{\textit{fully homomorphic encryption}} +\newacronym{mpc}{MPC}{\textit{multi-party computation}} +\newacronym{tee}{TEE}{\textit{trusted execution environment}} +\newacronym{sgx}{SGX}{\textit{Software Guard Extensions}} +\newacronym{zkp}{ZKP}{\textit{zero-knowledge proof}} +\newacronym{snark}{SNARK}{\textit{Succinct Non-interactive ARgument of Knowledge}} +\newacronym{stark}{STARK}{\textit{Scalable Transparent ARgument of Knowledge}} +\newacronym{zksnark}{zkSNARK}{\textit{zero-knowledge Succinct Non-interactive ARgument of Knowledge}} +\newacronym{zkstark}{zkSTARK}{\textit{zero-knowledge Scalable Transparent ARgument of Knowledge}} +\newacronym{mlops}{MLOps}{\textit{Machine Learning Operations}} +\newacronym{cicd}{CI/CD}{\textit{continuous integration/continuous deployment}} +\newacronym{dag}{DAG}{\textit{directed acyclic graph}} +\newacronym{etl}{ETL}{\textit{extract, transform, load}} +\newacronym{elt}{ELT}{\textit{extract, load, transform}} +\newacronym{oltp}{OLTP}{\textit{online transaction processing}} +\newacronym{olap}{OLAP}{\textit{online analytical processing}} +\newacronym{dwh}{DWH}{\textit{data warehouse}} +\newacronym{datalake}{Data Lake}{\textit{data lake}} +\newacronym{lakehouse}{Lakehouse}{\textit{lakehouse}} +\newacronym{edw}{EDW}{\textit{enterprise data warehouse}} +\newacronym{dim}{DIM}{\textit{dimension table}} +\newacronym{fact}{FACT}{\textit{fact table}} +\newacronym{scd}{SCD}{\textit{slowly changing dimension}} +\newacronym{star-schema}{Star Schema}{\textit{star schema}} +\newacronym{snowflake-schema}{Snowflake Schema}{\textit{snowflake schema}} +\newacronym{eer}{EER}{\textit{equal error rate}} +\newacronym{far}{FAR}{\textit{false acceptance rate}} +\newacronym{frr}{FRR}{\textit{false rejection rate}} +\newacronym{fpr}{FPR}{\textit{false positive rate}} +\newacronym{tpr}{TPR}{\textit{true positive rate}} +\newacronym{fnr}{FNR}{\textit{false negative rate}} +\newacronym{tnr}{TNR}{\textit{true negative rate}} +\newacronym{ppv}{PPV}{\textit{positive predictive value}} +\newacronym{npv}{NPV}{\textit{negative predictive value}} +\newacronym{auroc}{AUROC}{\textit{area under the receiver operating characteristic curve}} +\newacronym{auprc}{AUPRC}{\textit{area under the precision-recall curve}} +\newacronym{roc}{ROC}{\textit{receiver operating characteristic}} +\newacronym{pr-curve}{PR Curve}{\textit{precision-recall curve}} +\newacronym{f1}{F1}{\textit{F1 score}} +\newacronym{f2}{F2}{\textit{F2 score}} +\newacronym{fbeta}{F-beta}{\textit{F-beta score}} +\newacronym{mcc}{MCC}{Matthews \textit{correlation coefficient}} +\newacronym{iou}{IoU}{\textit{intersection over union}} +\newacronym{jaccard}{Jaccard}{Jaccard \textit{index}} +\newacronym{hausdorff}{Hausdorff}{Hausdorff \textit{distance}} +\newacronym{mse}{MSE}{\textit{mean squared error}} +\newacronym{rmse}{RMSE}{\textit{root mean squared error}} +\newacronym{mae}{MAE}{\textit{mean absolute error}} +\newacronym{mape}{MAPE}{\textit{mean absolute percentage error}} +\newacronym{smape}{SMAPE}{\textit{symmetric mean absolute percentage error}} +\newacronym{r2}{R²}{\textit{coefficient of determination}} +\newacronym{adjusted-r2}{Adjusted R²}{\textit{adjusted coefficient of determination}} +\newacronym{aic}{AIC}{Akaike \textit{information criterion}} +\newacronym{bic}{BIC}{Bayesian \textit{information criterion}} +\newacronym{cv}{CV}{\textit{cross-validation}} +\newacronym{loocv}{LOOCV}{\textit{leave-one-out cross-validation}} +\newacronym{kfold}{k-fold}{\textit{k-fold cross-validation}} +\newacronym{smote}{SMOTE}{\textit{Synthetic Minority Over-sampling Technique}} +\newacronym{adasyn}{ADASYN}{\textit{Adaptive Synthetic Sampling}} +\newacronym{enn}{ENN}{\textit{edited nearest neighbors}} +\newacronym{ncr}{NCR}{\textit{neighborhood cleaning rule}} +\newacronym{rus}{RUS}{\textit{random under-sampling}} +\newacronym{ros}{ROS}{\textit{random over-sampling}} +\newacronym{bce}{BCE}{\textit{binary cross-entropy}} +\newacronym{cce}{CCE}{\textit{categorical cross-entropy}} +\newacronym{scce}{SCCE}{\textit{sparse categorical cross-entropy}} +\newacronym{nll}{NLL}{\textit{negative log-likelihood}} +\newacronym{huber}{Huber}{Huber \textit{loss}} +\newacronym{focal}{Focal}{\textit{focal loss}} +\newacronym{dice-loss}{Dice Loss}{Dice \textit{loss}} +\newacronym{tversky}{Tversky}{Tversky \textit{loss}} +\newacronym{lovasz}{Lovász}{Lovász \textit{loss}} +\newacronym{bn}{BN}{\textit{batch normalization}} +\newacronym{ln}{LN}{\textit{layer normalization}} +\newacronym{in}{IN}{\textit{instance normalization}} +\newacronym{gn}{GN}{\textit{group normalization}} +\newacronym{wn}{WN}{\textit{weight normalization}} +\newacronym{sn}{SN}{\textit{spectral normalization}} +\newacronym{rmsnorm}{RMSNorm}{\textit{root mean square layer normalization}} +\newacronym{adain}{AdaIN}{\textit{adaptive instance normalization}} +\newacronym{adagn}{AdaGN}{\textit{adaptive group normalization}} +\newacronym{spade}{SPADE}{\textit{Spatially-Adaptive Normalization}} +\newacronym{radam}{RAdam}{\textit{Rectified Adam}} +\newacronym{lamb}{LAMB}{\textit{Layer-wise Adaptive Moments optimizer for Batch training}} +\newacronym{lars}{LARS}{\textit{Layer-wise Adaptive Rate Scaling}} +\newacronym{lookahead}{Lookahead}{Lookahead \textit{optimizer}} +\newacronym{swats}{SWATS}{\textit{Switching from Adam to SGD}} +\newacronym{slr}{SLR}{\textit{stochastic learning rate}} +\newacronym{tta}{TTA}{\textit{test-time augmentation}} +\newacronym{ema}{EMA}{\textit{exponential moving average}} +\newacronym{swa}{SWA}{\textit{stochastic weight averaging}} +\newacronym{ptq}{PTQ}{\textit{post-training quantization}} +\newacronym{qat}{QAT}{\textit{quantization-aware training}} +\newacronym{int8}{INT8}{\textit{8-bit integer}} +\newacronym{fp16}{FP16}{\textit{16-bit floating point}} +\newacronym{bf16}{BF16}{\textit{bfloat16}} +\newacronym{fp32}{FP32}{\textit{32-bit floating point}} +\newacronym{amp}{AMP}{\textit{automatic mixed precision}} +\newacronym{ode}{ODE}{\textit{ordinary differential equation}} +\newacronym{pde}{PDE}{\textit{partial differential equation}} +\newacronym{sde}{SDE}{\textit{stochastic differential equation}} +\newacronym{node}{NODE}{\textit{neural ordinary differential equation}} +\newacronym{anode}{ANODE}{\textit{augmented neural ordinary differential equation}} +\newacronym{pinn}{PINN}{\textit{physics-informed neural network}} +\newacronym{deeponet}{DeepONet}{\textit{Deep Operator Network}} +\newacronym{gnn}{GNN}{\textit{graph neural network}} +\newacronym{gcn}{GCN}{\textit{graph convolutional network}} +\newacronym{gat}{GAT}{\textit{graph attention network}} +\newacronym{gin}{GIN}{\textit{Graph Isomorphism Network}} +\newacronym{mpnn}{MPNN}{\textit{message passing neural network}} +\newacronym{gatedgcn}{Gated GCN}{\textit{gated graph convolutional network}} +\newacronym{line}{LINE}{\textit{Large-scale Information Network Embedding}} +\newacronym{gran}{GRAN}{\textit{Graph Recurrent Attention Network}} +\newacronym{dgmg}{DGMG}{\textit{Deep Generative Model of Graphs}} +\newacronym{molgan}{MolGAN}{\textit{Molecular Generative Adversarial Network}} +\newacronym{jtvae}{JT-VAE}{\textit{Junction Tree Variational Autoencoder}} +\newacronym{rgcn}{R-GCN}{\textit{Relational Graph Convolutional Network}} +\newacronym{hgt}{HGT}{\textit{Heterogeneous Graph Transformer}} +\newacronym{han}{HAN}{\textit{Heterogeneous Graph Attention Network}} +\newacronym{rgat}{R-GAT}{\textit{Relational Graph Attention Network}} +\newacronym{compgcn}{CompGCN}{\textit{Composition-based Graph Convolutional Network}} +\newacronym{kg}{KG}{\textit{knowledge graph}} +\newacronym{kge}{KGE}{\textit{knowledge graph embedding}} +\newacronym{kgc}{KGC}{\textit{knowledge graph completion}} +\newacronym{qa}{QA}{\textit{question answering}} +\newacronym{vqa}{VQA}{\textit{visual question answering}} +\newacronym{squad}{SQuAD}{\textit{Stanford Question Answering Dataset}} +\newacronym{glue}{GLUE}{\textit{General Language Understanding Evaluation}} +\newacronym{superglue}{SuperGLUE}{\textit{Super General Language Understanding Evaluation}} +\newacronym{xnli}{XNLI}{\textit{Cross-lingual Natural Language Inference}} +\newacronym{mnli}{MNLI}{\textit{Multi-Genre Natural Language Inference}} +\newacronym{snli}{SNLI}{Stanford \textit{Natural Language Inference}} +\newacronym{qqp}{QQP}{Quora Question Pairs} +\newacronym{qnli}{QNLI}{\textit{Question Natural Language Inference}} +\newacronym{sts}{STS}{\textit{Semantic Textual Similarity}} +\newacronym{cola}{CoLA}{\textit{Corpus of Linguistic Acceptability}} +\newacronym{sst}{SST}{Stanford \textit{Sentiment Treebank}} +\newacronym{mrpc}{MRPC}{Microsoft Research Paraphrase Corpus} +\newacronym{rte}{RTE}{\textit{Recognizing Textual Entailment}} +\newacronym{wnli}{WNLI}{Winograd \textit{Natural Language Inference}} +\newacronym{wsc}{WSC}{Winograd \textit{Schema Challenge}} +\newacronym{copa}{COPA}{\textit{Choice of Plausible Alternatives}} +\newacronym{piqa}{PIQA}{\textit{Physical Interaction Question Answering}} +\newacronym{siqa}{SIQA}{\textit{Social Interaction Question Answering}} +\newacronym{commonsenseqa}{CommonsenseQA}{CommonsenseQA} +\newacronym{arc}{ARC}{\textit{AI2 Reasoning Challenge}} +\newacronym{race}{RACE}{\textit{ReAding Comprehension from Examinations}} +\newacronym{drop}{DROP}{\textit{Discrete Reasoning Over Paragraphs}} +\newacronym{hotpotqa}{HotpotQA}{HotpotQA} +\newacronym{naturalquestions}{Natural Questions}{Natural Questions} +\newacronym{triviaqa}{TriviaQA}{TriviaQA} +\newacronym{quac}{QuAC}{\textit{Question Answering in Context}} +\newacronym{coqa}{CoQA}{\textit{Conversational Question Answering}} +\newacronym{multirc}{MultiRC}{\textit{Multi-Sentence Reading Comprehension}} +\newacronym{record}{ReCoRD}{\textit{Reading Comprehension with Commonsense Reasoning Dataset}} +\newacronym{wmt}{WMT}{\textit{Workshop on Machine Translation}} +\newacronym{iwslt}{IWSLT}{\textit{International Workshop on Spoken Language Translation}} \ No newline at end of file