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Found 23 papers, 11 papers with code
ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design
Comprehensive experiments demonstrate that ProteinWeaver: (1) generates high-quality, novel protein backbones through versatile domain assembly; (2) outperforms RFdiffusion, the current state-of-the-art in backbone design, by 13\% and 39\% for long-chain proteins; (3) shows the potential for cooperative function design through illustrative case studies.
DPLM-2: A Multimodal Diffusion Protein Language Model
In this paper, we introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures.
ProteinBench: A Holistic Evaluation of Protein Foundation Models
Recent years have witnessed a surge in the development of protein foundation models, significantly improving performance in protein prediction and generative tasks ranging from 3D structure prediction and protein design to conformational dynamics.
Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization
Antibody design, a crucial task with significant implications across various disciplines such as therapeutics and biology, presents considerable challenges due to its intricate nature.
Diffusion Language Models Are Versatile Protein Learners
This paper introduces diffusion protein language model (DPLM), a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences.
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.
DINOISER: Diffused Conditional Sequence Learning by Manipulating Noises
In this paper, we introduce DINOISER to facilitate diffusion models for sequence generation by manipulating noises.
Structure-informed Language Models Are Protein Designers
This paper demonstrates that language models are strong structure-based protein designers.
Helping the Weak Makes You Strong: Simple Multi-Task Learning Improves Non-Autoregressive Translators
Recently, non-autoregressive (NAR) neural machine translation models have received increasing attention due to their efficient parallel decoding.
The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21
This paper describes the Volctrans' submission to the WMT21 news translation shared task for German->English translation.
DirectQE: Direct Pretraining for Machine Translation Quality Estimation
However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly.
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation
Thus REDER enables reversible machine translation by simply flipping the input and output ends.
Information-theoretic Vocabularization via Optimal Transport
In this paper, we find an exciting relation between an information-theoretic feature and the performance of NLP tasks such as machine translation with a given vocabulary.
Vocabulary Learning via Optimal Transport for Neural Machine Translation
The choice of token vocabulary affects the performance of machine translation.
RPD: A Distance Function Between Word Embeddings
Based on the properties of RPD, we study the relations of word embeddings of different algorithms systematically and investigate the influence of different training processes and corpora.
Mirror-Generative Neural Machine Translation
Training neural machine translation models (NMT) requires a large amount of parallel corpus, which is scarce for many language pairs.
Towards Making the Most of Context in Neural Machine Translation
Document-level machine translation manages to outperform sentence level models by a small margin, but have failed to be widely adopted.
Multi-Perspective Inferrer: Reasoning Sentences Relationship from Holistic Perspective
Intuitively, NLI should rely more on multiple perspectives to form a holistic view to eliminate bias.
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering
However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data.
Dynamic Past and Future for Neural Machine Translation
Previous studies have shown that neural machine translation (NMT) models can benefit from explicitly modeling translated (Past) and untranslated (Future) to groups of translated and untranslated contents through parts-to-wholes assignment.
Learning to Discriminate Noises for Incorporating External Information in Neural Machine Translation
Previous studies show that incorporating external information could improve the translation quality of Neural Machine Translation (NMT) systems.
Modeling Past and Future for Neural Machine Translation
The Past and Future contents are fed to both the attention model and the decoder states, which offers NMT systems the knowledge of translated and untranslated contents.
Neural Machine Translation with Word Predictions
In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence. These vectors are generated by parameters which are updated by back-propagation of translation errors through time.
