Search Results for author:
Found 7 papers, 3 papers with code
JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients
We propose a practical approach to JPEG image decoding, utilizing a local implicit neural representation with continuous cosine formulation.
Universal Effectiveness of High-Depth Circuits in Variational Eigenproblems
We explore the effectiveness of variational quantum circuits in simulating the ground states of quantum many-body Hamiltonians.
Consistency of a Recurrent Language Model With Respect to Incomplete Decoding
Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition.
Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks
To tackle this problem we introduce a novel concept of a trainable gate function.
How Compact?: Assessing Compactness of Representations through Layer-Wise Pruning
This kind of analysis can help us identify the most compact representation within a deep neural network.
Comparing Sample-wise Learnability Across Deep Neural Network Models
We propose a measure of the learnability of a sample with a given deep neural network (DNN) model.
Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization
In recent years, a number of artificial intelligent services have been developed such as defect detection system or diagnosis system for customer services.
