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This enriches the audio encoder with visual information and the encoder can be used for evaluation without the visual modality.
Decompressive craniectomy (DC) is a common surgical procedure consisting of the removal of a portion of the skull that is performed after incidents such as stroke, traumatic brain injury (TBI) or other events that could result in acute subdural hemorrhage and/or increasing intracranial pressure.
Enabling robust intelligence in the wild entails learning systems that offer uninterrupted inference while affording sustained training, with varying amounts of data & supervision.
In this work, we propose a novel framework, Inference Stage Optimization (ISO), for improving the generalizability of 3D pose models when source and target data come from different pose distributions.
To overcome this issue, we propose Noise2Filter, a learned filter method that can be trained using only the measured data, and does not require any additional training data.
We present a differentiable dynamics solver that is able to handle frictional contact for rigid and deformable objects within a unified framework.
We achieve such commonsense reasoning by constructing pair-wise contrastive auxiliary predictions.
It involves first pre-training a model on a large amount of unlabeled data, then adapting the model to target tasks of interest.
These responses are processed by a second stage (analogous to cortical area V2) consisting of convolutional filters followed by half-wave rectification and pooling to generate V2 'complex cell' responses.