Alexander Mehta

Alexander Mehta, William Yang

In the task of emotion recognition from videos, a key improvement has been to focus on emotions over time rather than a single frame. There are many architectures to address this task such as GRUs, LSTMs, Self-Attention, Transformers, and Temporal Convolutional Networks (TCNs). However, these methods suffer from high memory usage, large amounts of operations, or poor gradients. We propose a method known as Neighborhood Attention with Convolutions TCN (NAC-TCN) which incorporates the benefits of attention and Temporal Convolutional Networks while ensuring that causal relationships are understood which results in a reduction in computation and memory cost. We accomplish this by introducing a causal version of Dilated Neighborhood Attention while incorporating it with convolutions. Our model achieves comparable, better, or state-of-the-art performance over TCNs, TCAN, LSTMs, and GRUs while requiring fewer parameters on standard emotion recognition datasets. We publish our code online for easy reproducibility and use in other projects.

Alexander Mehta, Ritik Jalisatgi

An estimated 253 million people have visual impairments. These visual impairments affect everyday lives, and limit their understanding of the outside world. This can pose a risk to health from falling or collisions. We propose a solution to this through quick and detailed communication of environmental spatial geometry through sound, providing the blind and visually impaired the ability to understand their spatial environment through sound technology. The model consists of fast object detection and 3D environmental mapping, which is communicated through a series of quick sound notes. These sound notes are at different frequencies, pitches, and arrangements in order to precisely communicate the depth and location of points within the environment. Sounds are communicated in the form of musical notes in order to be easily recognizable and distinguishable. A unique algorithm is used to segment objects, providing minimal accuracy loss and improvement from the normal O(n2 ) to O(n) (which is significant, as N in point clouds can often be in the range of 105 ). In testing, we achieved an R-value of 0.866 on detailed objects and an accuracy of 87.5% on an outdoor scene at night with large amounts of noise. We also provide a supplementary video demo of our system.

Alexander Mehta, Ruangrawee Kitichotkul, Vivek K Goyal et al.

Equivariant imaging (EI) enables training signal reconstruction models without requiring ground truth data by leveraging signal symmetries. Deep equilibrium models (DEQs) are a powerful class of neural networks where the output is a fixed point of a learned operator. However, training DEQs with complex EI losses requires implicit differentiation through fixed-point computations, whose implementation can be challenging. We show that backpropagation can be implemented modularly, simplifying training. Experiments demonstrate that DEQs trained with implicit differentiation outperform those trained with Jacobian-free backpropagation and other baseline methods. Additionally, we find evidence that EI-trained DEQs approximate the proximal map of an invariant prior.