CVIT's submissions to WAT-2019

2019-11-01 · WS 2019 11 · Jerin Philip, Shashank Siripragada, Upendra Kumar, Vinay Namboodiri, C. V. Jawahar

This paper describes the Neural Machine Translation systems used by IIIT Hyderabad (CVIT-MT) for the translation tasks part of WAT-2019. We participated in tasks pertaining to Indian languages and submitted results for English-Hindi, Hindi-English, English-Tamil and Tamil-English language pairs. We employ Transformer architecture experimenting with multilingual models and methods for low-resource languages.

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Machine Translation Translation

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Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…

Position-Wise Feed-Forward Layer 설명 없음

Residual Connection 설명 없음

BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…

ReLU How Do I Communicate to Expedia? How Do I Communicate to Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Live Support & Special Travel…

CVIT-MT Systems for WAT-2018

2019-03-19 · PACLIC 2018 12 · Jerin Philip, Vinay P. Namboodiri, C. V. Jawahar

This document describes the machine translation system used in the submissions of IIIT-Hyderabad CVIT-MT for the WAT-2018 English-Hindi translation task. Performance is evaluated on the associated corpus provided by the …

Machine Translation Translation

StrLoRA: Towards Streaming Continual Visual Instruction Tuning for MLLMs

2026-05-08 · Chang Che, Ziqi Wang, Hui Ma, Cheems Wang 외 arxiv

Continual Visual Instruction Tuning (CVIT) enables Multimodal Large Language Models to incrementally acquire new abilities. However, existing CVIT methods operate under a restrictive task-incremental setting, where each …

MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention

2022-11-25 · ICCV 2023 1 · Wenxuan Zeng, Meng Li, Wenjie Xiong, Tong Tong 외

Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference. However, existing neural network architectures, including Vision Tr…

Knowledge Distillation Neural Architecture Search

CViT: Continuous Vision Transformer for Operator Learning

2024-05-22 · Sifan Wang, Jacob H Seidman, Shyam Sankaran, Hanwen Wang 외

Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various physical domains. Here we introduce the…

Operator learning

CascadedViT: Cascaded Chunk-FeedForward and Cascaded Group Attention Vision Transformer

2025-11-18 · Srivathsan Sivakumar, Faisal Z. Qureshi arxiv

Vision Transformers (ViTs) have demonstrated remarkable performance across a range of computer vision tasks; however, their high computational, memory, and energy demands hinder deployment on resource-constrained platfor…