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Found 13 papers, 7 papers with code
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
Logical extrapolation can be achieved through recurrent systems, which can be iterated many times to solve difficult reasoning problems.
Thinking Deeper With Recurrent Networks: Logical Extrapolation Without Overthinking
Classical machine learning systems perform best when they are trained and tested on the same distribution, and they lack a mechanism to increase model power after training is complete.
Datasets for Studying Generalization from Easy to Hard Examples
We describe new datasets for studying generalization from easy to hard examples.
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data
Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications.
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
In this work, we show that recurrent networks trained to solve simple problems with few recurrent steps can indeed solve much more complex problems simply by performing additional recurrences during inference.
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
We devise a hybrid deep learning approach to solving tabular data problems.
The Uncanny Similarity of Recurrence and Depth
It is widely believed that deep neural networks contain layer specialization, wherein neural networks extract hierarchical features representing edges and patterns in shallow layers and complete objects in deeper layers.
Just How Toxic is Data Poisoning? A Benchmark for Backdoor and Data Poisoning Attacks
Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference.
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance.
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference.
Headless Horseman: Adversarial Attacks on Transfer Learning Models
We first demonstrate successful transfer attacks against a victim network using \textit{only} its feature extractor.
Adversarial Attacks on Machine Learning Systems for High-Frequency Trading
Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain.
Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory
We empirically evaluate common assumptions about neural networks that are widely held by practitioners and theorists alike.
