Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Min, Seonwoo"'
To overcome the quadratic cost of self-attention, recent works have proposed various sparse attention modules, most of which fall under one of two groups: 1) sparse attention under a hand-crafted patterns and 2) full attention followed by a sparse va
Externí odkaz:
http://arxiv.org/abs/2210.15541
Reducing the representational discrepancy between source and target domains is a key component to maximize the model generalization. In this work, we advocate for leveraging natural language supervision for the domain generalization task. We introduc
Externí odkaz:
http://arxiv.org/abs/2207.10285
Autor:
Kim, Jinwoo, Nguyen, Tien Dat, Min, Seonwoo, Cho, Sungjun, Lee, Moontae, Lee, Honglak, Hong, Seunghoon
We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat all nodes and edges as independent tokens, augment them with token emb
Externí odkaz:
http://arxiv.org/abs/2207.02505
We improve the recently developed Neural DUDE, a neural network-based adaptive discrete denoiser, by combining it with the supervised learning framework. Namely, we make the supervised pre-training of Neural DUDE compatible with the adaptive fine-tun
Externí odkaz:
http://arxiv.org/abs/2111.12350
Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge.
Externí odkaz:
http://arxiv.org/abs/2107.11381
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount
Externí odkaz:
http://arxiv.org/abs/1912.05625
We propose an application of sequence generative adversarial networks (SeqGAN), which are generative adversarial networks for discrete sequence generation, for creating polyphonic musical sequences. Instead of a monophonic melody generation suggested
Externí odkaz:
http://arxiv.org/abs/1710.11418
We present a new framework of applying deep neural networks (DNN) to devise a universal discrete denoiser. Unlike other approaches that utilize supervised learning for denoising, we do not require any additional training data. In such setting, while
Externí odkaz:
http://arxiv.org/abs/1605.07779
Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology. miRNAs are usually short in length ranging between 20 and 23 base pairs. It is
Externí odkaz:
http://arxiv.org/abs/1605.00017