Deep Learning
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
The linear mapping in deep neural networks (DNNs) are mostly realized in the form of fully connected (FC) or convolutional (CONV) layers.
CAPE
ะขะฐัััะฝะฐ ะะธั ะพะผะฐะฝะตะฝะบะพ
Since the appearance of Transformers, many works have investigated ways to encode positional information.
A Appendix
A.1 Recurrent neural networks A.1.1 T ask structures One trial has T = 1400 time steps. All input conditions are trained simultaneously with equal probability to ensure a balanced training set (batch size = 400). In total, we defined P = 8 15 = 120 sampling locations. The sine wave generation network has 51 different input conditions (51 input levels). The respective local and global operative dimensions are defined as explained in section 2.2.1.
Operative dimensions in unconstrained connectivity of recurrent neural networks
Previous work derived such an understanding for RNNs fulfilling very specific constraints on their connectivity, but it is unclear whether the resulting insights apply more generally.
Efficient Generation of Structured Objects with Constrained Adversarial Networks Luca Di Liello University of Trento Pierfrancesco Ardino University of Trento Jacopo Gobbi
The issue is that structured objects must satisfy hard requirements (e.g., molecules must be chemically valid) that are difficult to acquire
FIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives
Figure 1: We depict five core desiderata for VQA models with associated metrics and objectives.
