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.
Tag: Share My Research
Huawei and University of Toronto | All at Once: Temporally Adaptive Multi-Frame Interpolation with Advanced Motion Modeling
This research proposes an efficient and cost-effective solution for multi-frame video interpolation.
Deep Claim: Payer Response Prediction from Claims Data with Deep Learning
The paper outlines a predictive model we’ve developed that has the potential to help significantly reduce wasteful healthcare spending.
Deep Graph Contrastive Representation Learning
This paper presents a novel contrastive framework for unsupervised graph representation learning.
Tsinghua & UC Berkeley | A New Meta-Baseline for Few-Shot Learning
A new simple baseline for few-shot learning that achieves state-of-the-art performance.
Cross-domain Correspondence Learning for Exemplar-based Image Translation
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.
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation
This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture.
Deflecting Adversarial Attacks
Researchers introduce the notion of deflecting adversarial attacks, which presents a step towards ending the battle between attacks and defenses.
Impact of ImageNet Model Selection on Domain Adaptation
Researchers investigate how different ImageNet models affect transfer accuracy on domain adaptation problems.
BERT-of-Theseus: Compressing BERT by Progressive Module Replacing
Researchers propose a novel model compression approach to effectively compress BERT by progressive module replacing.
Self-Adversarial Learning with Comparative Discrimination for Text Generation
Researchers have proposed a novel self-adversarial learning (SAL) paradigm for improving GANs’ performance in text generation.
Batchboost: Regularization for Stabilizing Training With Resistance to Underfitting & Overfitting
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.