Tunable Stiffness Enables Fast and Efficient Swimming in Fish-like Robots
This paper uncovers the secrets of highly efficient swimming at varying speeds which could inform the design of next generation underwater drones.
AI Technology & Industry Review
This paper uncovers the secrets of highly efficient swimming at varying speeds which could inform the design of next generation underwater drones.
This research proposes an efficient and cost-effective solution for multi-frame video interpolation.
The paper outlines a predictive model we’ve developed that has the potential to help significantly reduce wasteful healthcare spending.
This paper presents a novel contrastive framework for unsupervised graph representation learning.
A new simple baseline for few-shot learning that achieves state-of-the-art performance.
We present a general framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain, given an exemplar image.
This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture.
Researchers introduce the notion of deflecting adversarial attacks, which presents a step towards ending the battle between attacks and defenses.
Researchers investigate how different ImageNet models affect transfer accuracy on domain adaptation problems.
Researchers propose a novel model compression approach to effectively compress BERT by progressive module replacing.
Researchers have proposed a novel self-adversarial learning (SAL) paradigm for improving GANs’ performance in text generation.
Batchboost is a simple technique to accelerate ML model training by adaptively feeding mini-batches with artificial samples which are created by mixing two examples from the previous step – in favor of pairing those that produce the difficult one.