150 citations · 169 across the 7 of their papers we have counts for
9 papers
Adaptive Hierarchical Decomposition of Large Deep Networks
Sumanth Chennupati, Sai Nooka, Shagan Sah +1
Deep learning has recently demonstrated its ability to rival the human brain for visual object recognition. As datasets get larger, a natural question to ask is if existing deep le…
Fully Convolutional Networks for Handwriting Recognition
Felipe Petroski Such, Dheeraj Peri, Frank Brockler +2
Handwritten text recognition is challenging because of the virtually infinite ways a human can write the same message. Our fully convolutional handwriting model takes in a handwrit…
Show, Translate and Tell
Dheeraj Peri, Shagan Sah, Raymond Ptucha
Humans have an incredible ability to process and understand information from multiple sources such as images, video, text, and speech. Recent success of deep neural networks has en…
Vector Learning for Cross Domain Representations
Shagan Sah, Chi Zhang, Thang Nguyen +3
Recently, generative adversarial networks have gained a lot of popularity for image generation tasks. However, such models are associated with complex learning mechanisms and deman…
Semantically Invariant Text-to-Image Generation
Shagan Sah, Dheeraj Peri, Ameya Shringi +4
Image captioning has demonstrated models that are capable of generating plausible text given input images or videos. Further, recent work in image generation has shown significant…
Batch-normalized Recurrent Highway Networks
Chi Zhang, Thang Nguyen, Shagan Sah +3
Gradient control plays an important role in feed-forward networks applied to various computer vision tasks. Previous work has shown that Recurrent Highway Networks minimize the pro…
Semantic Sentence Embeddings for Paraphrasing and Text Summarization
Chi Zhang, Shagan Sah, Thang Nguyen +4
This paper introduces a sentence to vector encoding framework suitable for advanced natural language processing. Our latent representation is shown to encode sentences with common…
Robust Spatial Filtering with Graph Convolutional Neural Networks
Felipe Petroski Such, Shagan Sah, Miguel Dominguez +5
Convolutional Neural Networks (CNNs) have recently led to incredible breakthroughs on a variety of pattern recognition problems. Banks of finite impulse response filters are learne…
Neural Networks with Manifold Learning for Diabetic Retinopathy Detection
Arjun Raj Rajanna, Kamelia Aryafar, Rajeev Ramchandran +3
Widespread outreach programs using remote retinal imaging have proven to decrease the risk from diabetic retinopathy, the leading cause of blindness in the US. However, this proces…