Deep Learning
Learning to Synthesize Programs as Interpretable and Generalizable Policies Dweep Trivedi Jesse Zhang 1 Shao-Hua Sun 1 Joseph J. Lim 1 1 University of Southern California
Y et, these works either employ limited policy representations (e.g.
The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models
Recent works have demonstrated great success in pre-training large-scale autore-gressive language models (e.g., GPT -3) on massive GPUs.
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning Jiani Huang
Deep learning and symbolic reasoning are complementary techniques for an intelligent system.
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks
Figure 2: Real data predicted vs. true results and category size distribution Python 3.8 Numpy + Pandas suite, Keras and Tensorflow Code is fully available in the lmmnn package on Github Running code: see details in package README file 3 n = 100, 000, σ At each run 80% (80,000) of the simulated data is used as training set, of which 10% (8,000) is used as validation set which the network only uses to check for early stopping. Embedding layer which maps q levels to a d = 0 .1 q vector, so input dimension is p + d - Physical activity (P A) definition: Subjects wore an accelerometer on their wrist for 7 days. ENMO in m-g was summarised across valid wear-time. ETL: We follow instructions by Pearce et al. (2020), implemented in R. At high level, we "once a week" is converted to 1 and "every day" is converted to 7. Finally the P A dependent variable is standardized to have a Baseline DNN architecture: Pearce et al. did not use DNNs, but two separate linear regressions, for men and women. ReLU activation of 10 and 5 neurons, followed by a single output neuron with no activation.
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks
A special scenario of interest is that of repeated measures, where the categorical feature is the identity of the individual or object, and each object is measured several times, possibly under different conditions (values of the other features).
Not All Low-Pass Filters are Robust in Graph Convolutional Networks Heng Chang 1, Yu Rong
Graph Convolutional Networks (GCNs) are promising deep learning approaches in learning representations for graph-structured data.
