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Found 5 papers, 1 papers with code
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following
ECL consists of: (i) an instruction parser that translates the natural languages into executable programs; (ii) an embodied concept learner that grounds visual concepts based on language descriptions; (iii) a map constructor that estimates depth and constructs semantic maps by leveraging the learned concepts; and (iv) a program executor with deterministic policies to execute each program.
Learning to Grow Pretrained Models for Efficient Transformer Training
Scaling transformers has led to significant breakthroughs in many domains, leading to a paradigm in which larger versions of existing models are trained and released on a periodic basis.
Generating Realistic Physical Adversarial Examplesby Patch Transformer Network
Physical adversarial attacks apply carefully crafted adversarial perturbations onto real objects to maliciously alter the prediction of object classifiers or detectors.
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution
First, we generate images in low-frequency bands by training a sampler in the wavelet domain.
On the Information Bottleneck Theory of Deep Learning
The practical successes of deep neural networks have not been matched by theoretical progress that satisfyingly explains their behavior.
