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Found 9 papers, 4 papers with code
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning
In this work, we propose \textbf{E2HQV}, a novel E2V paradigm designed to produce high-quality video frames from events.
LFACon: Introducing Anglewise Attention to No-Reference Quality Assessment in Light Field Space
In this paper, we propose a novel concept of "anglewise attention" by introducing a multihead self-attention mechanism to the angular domain of an LFI.
Patents and intellectual property assets as non-fungible tokens: key technologies and challenges
The proposed framework provides fundamental elements and guidance for businesses in taking advantage of NFTs in real-world problems such as grant patents, funding, biotechnology, and so forth.
Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning
The game of Chinese Checkers is a challenging traditional board game of perfect information that differs from other traditional games in two main aspects: first, unlike Chess, all checkers remain indefinitely in the game and hence the branching factor of the search tree does not decrease as the game progresses; second, unlike Go, there are also no upper bounds on the depth of the search tree since repetitions and backward movements are allowed.
NAIRS: A Neural Attentive Interpretable Recommendation System
In this paper, we develop a neural attentive interpretable recommendation system, named NAIRS.
Aspect and Sentiment Aware Abstractive Review Summarization
Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction.
N-fold Superposition: Improving Neural Networks by Reducing the Noise in Feature Maps
Finally, we replicate this new block into n copies and concatenate them as the input to the FC layer.
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network.
Generative Adversarial Network for Abstractive Text Summarization
In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization.
Ranked #5 on
Text Summarization
on CNN / Daily Mail (Anonymized)
Abstractive Text Summarization
Generative Adversarial Network
+2
