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- Learning to rank2,499
It is proposed that learning to rank should adopt the listwise approach in which lists of objects are used as 'instances' in learning, and introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning.
- BioGPT: generative pre-trained transformer for biomedical text generation and mining1,580
This paper proposes BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature and evaluates it on six biomedical natural language processing tasks and demonstrates that the model outperforms previous models on most tasks.
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- LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval528
This paper has constructed a benchmark dataset referred to as LETOR, derived the LETOR data from the existing data sets widely used in IR, namely, OHSUMED and TREC data and provided the results of several state-ofthe-arts learning to rank algorithms on the data.
- LETOR: A benchmark collection for research on learning to rank for information retrieval527
The details of the LETOR collection are described and it is shown how it can be used in different kinds of researches, and several state-of-the-art learning to rank algorithms on LETOR are compared.
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- NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models447
This work proposes NaturalSpeech 3, a TTS system with novel factorized diffusion models to generate natural speech in a zero-shot way that outperforms the state-of-the-art TTS systems on quality, similarity, prosody, and intelligibility, and achieves on-par quality with human recordings.
- NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers431
This paper develops NaturalSpeech 2, a TTS system that leverages a neural audio codec with residual vector quantizers to get the quantized latent vectors and uses a diffusion model to generate these latent vectors conditioned on text input.
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- AdaRNN389
This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data and proposes Temporal Distribution Characterization to better characterize the distribution information in the TS.
- Image-to-Image Translation: Methods and Applications377
The key techniques of the existing I2I works are analyzed and clarified to clarify the main progress the community has made and point out remaining challenges in related fields.
- NaturalSpeech: End-to-End Text-to-Speech Synthesis With Human-Level Quality359
A variational auto-encoder (VAE) for end-to-end text-to-waveform generation, with several key modules to enhance the capacity of the prior from text and reduce the complexity of the posterior from speech, including phoneme pre-training, differentiable duration modeling, bidirectional prior/posterior modeling, and a memory mechanism in VAE.
- An Empirical Study on Learning to Rank of Tweets 1310
This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet.
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- Feature selection for ranking286
This paper proposes a new feature selection method that uses its value to rank the training instances, and defines the ranking accuracy in terms of a performance measure or a loss function as the importance of the feature.
- A general approximation framework for direct optimization of information retrieval measures285
A general framework for direct optimization of IR measures, which enjoys several theoretical advantages, and experiments on benchmark datasets show that the algorithms deduced from the framework are very effective when compared to existing methods.
- FRank277
An algorithm named FRank is proposed based on a generalized additive model for the sake of minimizing the fedelity loss and learning an effective ranking function and the experimental results show that the proposed algorithm outperforms other learning-based ranking methods on both conventional IR problem and Web search.
- MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training224
This paper develops MusicBERT, a large-scale pre-trained model for music understanding that contains more than 1 million music songs and designs several mechanisms, including OctupleMIDI encoding and bar-level masking strategy, to enhance pre-training with symbolic music data.
- Deliberation Networks: Sequence Generation Beyond One-Pass Decoding223
This work introduces the deliberation process into the encoder-decoder framework and proposes deliberation networks for sequence generation and establishes a new state-of-the-art BLEU score on the WMT 2014 English-to-French translation task.
- Efficient Training of BERT by Progressively Stacking198
This paper explores an efficient training method for the state-of-the-art bidirectional Transformer (BERT) model and pro-poses the stacking algorithm to transfer knowledge from a shallow model to a deep model; then the algorithm is applied progressively to accelerate BERT training.
- A Study of Reinforcement Learning for Neural Machine Translation192
A systematic study on how to train better NMT models using reinforcement learning, providing a comprehensive comparison of several important factors and proposing a new method to leverage RL to further boost the performance of NMT systems trained with source/target monolingual data.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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