Search Results for author: Zehao Wang

Found 21 papers, 8 papers with code

Semi-Automatic Line-System Provisioning with Integrated Physical-Parameter-Aware Methodology: Field Verification and Operational Feasibility

no code implementations24 Mar 2024 Hideki Nishizawa, Giacomo Borraccini, Takeo Sasai, Yue-Kai Huang, Toru Mano, Kazuya Anazawa, Masatoshi Namiki, Soichiroh Usui, Tatsuya Matsumura, Yoshiaki Sone, Zehao Wang, Seiji Okamoto, Takeru Inoue, Ezra Ip, Andrea D'Amico, Tingjun Chen, Vittorio Curri, Ting Wang, Koji Asahi, Koichi Takasugi

We propose methods and an architecture to conduct measurements and optimize newly installed optical fiber line systems semi-automatically using integrated physics-aware technologies in a data center interconnection (DCI) transmission scenario.

FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs

no code implementations8 Jan 2024 Shulin Zeng, Jun Liu, Guohao Dai, Xinhao Yang, Tianyu Fu, Hongyi Wang, Wenheng Ma, Hanbo Sun, Shiyao Li, Zixiao Huang, Yadong Dai, Jintao Li, Zehao Wang, Ruoyu Zhang, Kairui Wen, Xuefei Ning, Yu Wang

However, existing GPU and transformer-based accelerators cannot efficiently process compressed LLMs, due to the following unresolved challenges: low computational efficiency, underutilized memory bandwidth, and large compilation overheads.

Computational Efficiency Language Modelling +2

TeTriRF: Temporal Tri-Plane Radiance Fields for Efficient Free-Viewpoint Video

no code implementations10 Dec 2023 Minye Wu, Zehao Wang, Georgios Kouros, Tinne Tuytelaars

Neural Radiance Fields (NeRF) revolutionize the realm of visual media by providing photorealistic Free-Viewpoint Video (FVV) experiences, offering viewers unparalleled immersion and interactivity.

DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection

1 code implementation12 Oct 2023 Zehao Wang, Yiwen Guo, Qizhang Li, Guanglei Yang, WangMeng Zuo

Most existing data augmentation methods tend to find a compromise in augmenting the data, \textit{i. e.}, increasing the amplitude of augmentation carefully to avoid degrading some data too much and doing harm to the model performance.

Data Augmentation Image Classification +1

Fast WDM provisioning with minimal probing: the first field experiments for DC exchanges

no code implementations14 Sep 2023 Hideki Nishizawa, Toru Mano, Thomas Ferreira de Lima, Yue-Kai Huang, Zehao Wang, Wataru Ishida, Masahisa Kawashima, Ezra Ip, Andrea D'Amico, Seiji Okamoto, Takeru Inoue, Kazuya Anazawa, Vittorio Curri, Gil Zussman, Daniel Kilper, Tingjun Chen, Ting Wang, Koji Asahi, Koichi Takasugi

Then, using field fibers deployed in the NSF COSMOS testbed (deployed in an urban area), a Linux-based transmission device software architecture, and coherent transceivers with different optical frequency ranges, modulators, and modulation formats, the fast WDM provisioning of an optical path was completed within 6 minutes (with a Q-factor error of about 0. 7 dB).

Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain

no code implementations4 Aug 2023 Agastya Raj, Zehao Wang, Frank Slyne, Tingjun Chen, Dan Kilper, Marco Ruffini

We present a novel ML framework for modeling the wavelength-dependent gain of multiple EDFAs, based on semi-supervised, self-normalizing neural networks, enabling one-shot transfer learning.

Transfer Learning

Few-shot Event Detection: An Empirical Study and a Unified View

1 code implementation3 May 2023 Yubo Ma, Zehao Wang, Yixin Cao, Aixin Sun

Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e. g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress. This paper presents a thorough empirical study, a unified view of ED models, and a better unified baseline.

Event Detection

From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding

no code implementations2 Apr 2023 Yong-Lu Li, Xiaoqian Wu, Xinpeng Liu, Zehao Wang, Yiming Dou, Yikun Ji, Junyi Zhang, Yixing Li, Jingru Tan, Xudong Lu, Cewu Lu

By aligning the classes of previous datasets to our semantic space, we gather (image/video/skeleton/MoCap) datasets into a unified database in a unified label system, i. e., bridging "isolated islands" into a "Pangea".

Action Understanding Transfer Learning

Sepformer-based Models: More Efficient Models for Long Sequence Time-Series Forecasting

1 code implementation IEEE Transactions on Emerging Topics in Computing 2022 Jin Fan, Zehao Wang, Danfeng Sun, Huifeng Wu

These include: 1) complexity - Informer has a relatively high computational complexity and a high memory overhead; 2) nuance - there is limited ability to capture the subtle features in a data stream; 3) interpretability - the inference procedure of Informer is not explainable; 4) extensibility - accuracy is poor with extra-long multivariate time series.

Time Series Time Series Forecasting

Layout-aware Dreamer for Embodied Referring Expression Grounding

1 code implementation30 Nov 2022 Mingxiao Li, Zehao Wang, Tinne Tuytelaars, Marie-Francine Moens

In this work, we study the problem of Embodied Referring Expression Grounding, where an agent needs to navigate in a previously unseen environment and localize a remote object described by a concise high-level natural language instruction.

Common Sense Reasoning Navigate +1

Find a Way Forward: a Language-Guided Semantic Map Navigator

no code implementations7 Mar 2022 Zehao Wang, Mingxiao Li, Minye Wu, Marie-Francine Moens, Tinne Tuytelaars

In this paper, we introduce the map-language navigation task where an agent executes natural language instructions and moves to the target position based only on a given 3D semantic map.

Imitation Learning

Sparsity Winning Twice: Better Robust Generalization from More Efficient Training

1 code implementation ICLR 2022 Tianlong Chen, Zhenyu Zhang, Pengjun Wang, Santosh Balachandra, Haoyu Ma, Zehao Wang, Zhangyang Wang

We introduce two alternatives for sparse adversarial training: (i) static sparsity, by leveraging recent results from the lottery ticket hypothesis to identify critical sparse subnetworks arising from the early training; (ii) dynamic sparsity, by allowing the sparse subnetwork to adaptively adjust its connectivity pattern (while sticking to the same sparsity ratio) throughout training.

A frequency domain analysis of gradient-based adversarial examples

no code implementations1 Jan 2021 Bochen Lv, Pu Yang, Zehao Wang, Zhanxing Zhu

And the log-spectrum difference of the adversarial examples and clean image is more concentrated in the high-frequency part than the low-frequency part.

Exploring Inherent Properties of the Monophonic Melody of Songs

no code implementations20 Mar 2020 Zehao Wang, Shicheng Zhang, Xiaoou Chen

Unlike other components in music theory, such as harmony and counterpoint, computable features for melody is urgently in need.

Information Retrieval Retrieval +1

Information Compensation for Deep Conditional Generative Networks

no code implementations23 Jan 2020 Zehao Wang, Kaili Wang, Tinne Tuytelaars, Jose Oramas

In recent years, unsupervised/weakly-supervised conditional generative adversarial networks (GANs) have achieved many successes on the task of modeling and generating data.


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