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Found 21 papers, 10 papers with code
GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling
However, a fixed temperature parameter is used in most cases, which may not always be an optimal choice for balancing generation quality and diversity.
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
DecompOpt presents a new generation paradigm which combines optimization with conditional diffusion models to achieve desired properties while adhering to the molecular grammar.
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
Designing 3D ligands within a target binding site is a fundamental task in drug discovery.
Binding-Adaptive Diffusion Models for Structure-Based Drug Design
Then the selected protein-ligand subcomplex is processed with SE(3)-equivariant neural networks, and transmitted back to each atom of the complex for augmenting the target-aware 3D molecule diffusion generation with binding interaction information.
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
We then reprogram pretrained masked language models into diffusion language models via diffusive adaptation, wherein task-specific finetuning and instruction finetuning are explored to unlock their versatility in solving general language tasks.
Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation
Benefiting from the sequence-level knowledge distillation, the Non-Autoregressive Transformer (NAT) achieves great success in neural machine translation tasks.
DINOISER: Diffused Conditional Sequence Learning by Manipulating Noises
In this paper, we introduce DINOISER to facilitate diffusion models for sequence generation by manipulating noises.
SENDER: SEmi-Nonlinear Deep Efficient Reconstructor for Extraction Canonical, Meta, and Sub Functional Connectivity in the Human Brain
Deep Linear and Nonlinear learning methods have already been vital machine learning methods for investigating the hierarchical features such as functional connectivity in the human brain via functional Magnetic Resonance signals; however, there are three major shortcomings: 1).
DELMAR: Deep Linear Matrix Approximately Reconstruction to Extract Hierarchical Functional Connectivity in the Human Brain
Moreover, the theoretical analyses indicate that DELMAR can converge to the unique fixed point and even enable the accurate approximation of original input as DNNs.
DEMAND: Deep Matrix Approximately Nonlinear Decomposition to Identify Meta, Canonical, and Sub-Spatial Pattern of functional Magnetic Resonance Imaging in the Human Brain
At first, the proposed DEMAND employs a non-fully connected and multilayer-stacked architecture that is easier to be optimized compared with canonical DNNs; furthermore, due to the efficient architecture, training DEMAND can avoid overfitting and enables the recognition of individual/minor features based on a small dataset such as an individual data; finally, a novel rank estimator technique is introduced to tune all hyperparameters of DEMAND automatically.
SADAM: Stochastic Adam, A Stochastic Operator for First-Order Gradient-based Optimizer
In this work, to efficiently help escape the stationary and saddle points, we propose, analyze, and generalize a stochastic strategy performed as an operator for a first-order gradient descent algorithm in order to increase the target accuracy and reduce time consumption.
$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
Experimental quantum advantage with quantum coupon collector
The information transmitted by the proposed game also broke the classical limit.
Non-Autoregressive Translation by Learning Target Categorical Codes
Non-autoregressive Transformer is a promising text generation model.
Ranked #7 on
Machine Translation
on WMT2014 German-English
Non-iterative Parallel Text Generation via Glancing Transformer
Although non-autoregressive models with one-iteration generation achieves remarkable inference speed-up, they still falls behind their autoregressive counterparts inprediction accuracy.
Glancing Transformer for Non-Autoregressive Neural Machine Translation
With GLM, we develop Glancing Transformer (GLAT) for machine translation.
Ranked #69 on
Machine Translation
on WMT2014 English-German
Explicit Semantic Decomposition for Definition Generation
Definition generation, which aims to automatically generate dictionary definitions for words, has recently been proposed to assist the construction of dictionaries and help people understand unfamiliar texts.
Non-autoregressive Transformer by Position Learning
Non-autoregressive models are promising on various text generation tasks.
PNAT: Non-autoregressive Transformer by Position Learning
However, position modeling of output words is an essential problem in non-autoregressive text generation.
Generating Sentences from Disentangled Syntactic and Semantic Spaces
In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces.
