Search Results for author: Sosuke Kobayashi

Found 21 papers, 8 papers with code

SHAPE : Shifted Absolute Position Embedding for Transformers

no code implementations EMNLP 2021 Shun Kiyono, Sosuke Kobayashi, Jun Suzuki, Kentaro Inui

Position representation is crucial for building position-aware representations in Transformers.

On Layer Normalizations and Residual Connections in Transformers

no code implementations1 Jun 2022 Sho Takase, Shun Kiyono, Sosuke Kobayashi, Jun Suzuki

In the perspective of a layer normalization (LN) position, the architecture of Transformers can be categorized into two types: Post-LN and Pre-LN.

Text Generation

Decomposing NeRF for Editing via Feature Field Distillation

no code implementations31 May 2022 Sosuke Kobayashi, Eiichi Matsumoto, Vincent Sitzmann

Emerging neural radiance fields (NeRF) are a promising scene representation for computer graphics, enabling high-quality 3D reconstruction and novel view synthesis from image observations.

3D Reconstruction Novel View Synthesis

SHAPE: Shifted Absolute Position Embedding for Transformers

1 code implementation13 Sep 2021 Shun Kiyono, Sosuke Kobayashi, Jun Suzuki, Kentaro Inui

Position representation is crucial for building position-aware representations in Transformers.

Efficient Estimation of Influence of a Training Instance

no code implementations EMNLP (sustainlp) 2020 Sosuke Kobayashi, Sho Yokoi, Jun Suzuki, Kentaro Inui

Understanding the influence of a training instance on a neural network model leads to improving interpretability.

All Word Embeddings from One Embedding

1 code implementation NeurIPS 2020 Sho Takase, Sosuke Kobayashi

The proposed method, ALONE (all word embeddings from one), constructs the embedding of a word by modifying the shared embedding with a filter vector, which is word-specific but non-trainable.

Machine Translation Sentence Summarization +2

Train Sparsely, Generate Densely: Memory-efficient Unsupervised Training of High-resolution Temporal GAN

2 code implementations22 Nov 2018 Masaki Saito, Shunta Saito, Masanori Koyama, Sosuke Kobayashi

Training of Generative Adversarial Network (GAN) on a video dataset is a challenge because of the sheer size of the dataset and the complexity of each observation.

Video Generation

DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback

1 code implementation28 Oct 2018 Riku Arakawa, Sosuke Kobayashi, Yuya Unno, Yuta Tsuboi, Shin-ichi Maeda

A remedy for this is to train an agent with real-time feedback from a human observer who immediately gives rewards for some actions.

reinforcement-learning

Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions

no code implementations EMNLP 2018 Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu, Jun Suzuki, Kentaro Inui

As well as deriving PMI from mutual information, we derive this new measure from the Hilbert--Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC).

Machine Translation Sentence Embeddings +1

Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations

2 code implementations NAACL 2018 Sosuke Kobayashi

We stochastically replace words with other words that are predicted by a bi-directional language model at the word positions.

General Classification Language Modelling +3

Unsupervised Learning of Style-sensitive Word Vectors

no code implementations ACL 2018 Reina Akama, Kento Watanabe, Sho Yokoi, Sosuke Kobayashi, Kentaro Inui

This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner.

Word Embeddings

Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions

1 code implementation17 Oct 2017 Jun Hatori, Yuta Kikuchi, Sosuke Kobayashi, Kuniyuki Takahashi, Yuta Tsuboi, Yuya Unno, Wilson Ko, Jethro Tan

In this paper, we propose the first comprehensive system that can handle unconstrained spoken language and is able to effectively resolve ambiguity in spoken instructions.

object-detection Object Detection

A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse

1 code implementation IJCNLP 2017 Sosuke Kobayashi, Naoaki Okazaki, Kentaro Inui

This study addresses the problem of identifying the meaning of unknown words or entities in a discourse with respect to the word embedding approaches used in neural language models.

Language Modelling Word Embeddings

An RNN-based Binary Classifier for the Story Cloze Test

no code implementations WS 2017 Melissa Roemmele, Sosuke Kobayashi, Naoya Inoue, Andrew Gordon

In this paper we present a system that performs this task using a supervised binary classifier on top of a recurrent neural network to predict the probability that a given story ending is correct.

Cloze Test Word Embeddings

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