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.
Category: Share My Research
Fast Video Object Segmentation using the Global Context Module
A novel module that effectively and efficiently propagates information through an arbitrarily long video, with constant complexity w.r.t. number of frames and linear complexity w.r.t. resolution.
ICMI 2020 Best Paper | Gesticulator: A framework for semantically-aware speech-driven gesture generation
The research develop machine learning methods that enable virtual agents (such as avatars from a computer game) to communicate non-verbally.
UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus
UmlsBERT is a deep Transformer network architecture that incorporates clinical domain knowledge from a clinical Metathesaurus in order to build ‘semantically enriched’ contextual representations that will benefit from both the contextual learning and domain knowledge.
Drift with Devil: Security of Multi-Sensor Fusion based Localization in High-Level Autonomous Driving under GPS Spoofing
This paper is the first to systematically study the security of Multi-Sensor Fusion (MSF) based localization in high-autonomy Autonomous Vehicles (AVs). Researchers design FusionRipper, a novel and general attack that opportunistically captures and exploits take-over vulnerabilities.
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.
Bringing Old Photos Back to Life
The proposed method outperforms supervised methods and unsupervised translation methods on restoring real photos.
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.
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.