Search Results for author: Sepehr Jalali

Found 7 papers, 1 papers with code

How does BERT process disfluency?

no code implementations SIGDIAL (ACL) 2021 Ye Tian, Tim Nieradzik, Sepehr Jalali, Da-Shan Shiu

Analysis on sentence embeddings of disfluent and fluent sentence pairs reveals that the deeper the layer, the more similar their representation (exp2).

Sentence Sentence Embeddings +1

Optimal Order Simple Regret for Gaussian Process Bandits

no code implementations NeurIPS 2021 Sattar Vakili, Nacime Bouziani, Sepehr Jalali, Alberto Bernacchia, Da-Shan Shiu

Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$.

Art Analysis

Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridging

no code implementations21 May 2021 Philipp Ennen, Yen-Ting Lin, Ali Girayhan Ozbay, Ferdinando Insalata, Maolin Li, Ye Tian, Sepehr Jalali, Da-Shan Shiu

In light of the recent success of data-driven approaches, we propose the novel future bridging NLG (FBNLG) concept for dialogue systems and simulators.

Text Generation

Relationship-based Neural Baby Talk

no code implementations8 Mar 2021 Fan Fu, TingTing Xie, Ioannis Patras, Sepehr Jalali

Understanding interactions between objects in an image is an important element for generating captions.

Caption Generation Graph Attention

Cyclic orthogonal convolutions for long-range integration of features

no code implementations NeurIPS Workshop SVRHM 2021 Federica Freddi, Jezabel R Garcia, Michael Bromberg, Sepehr Jalali, Da-Shan Shiu, Alvin Chua, Alberto Bernacchia

We propose a novel architecture that allows flexible information flow between features $z$ and locations $(x, y)$ across the entire image with a small number of layers.

Image Classification Pathfinder

M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments

no code implementations19 Nov 2018 Tim Laibacher, Tillman Weyde, Sepehr Jalali

In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems.

Retinal Vessel Segmentation

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