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Found 10 papers, 3 papers with code
Towards Visual Syntactical Understanding
Through our experiments, we discover an intriguing property of DNNs where we observe that state-of-the-art convolutional neural networks, as well as vision transformers, fail to discriminate between syntactically correct and incorrect images when trained on only correct ones.
Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model
The segmented attention and lightweight RAF neurons ensure the efficiency of the proposed transformer.
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Implementation of MPPT Technique of Solar Module with Supervised Machine Learning
In this paper, we proposed a method using supervised ML in solar PV system for MPPT analysis.
One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency
We achieve top-1 accuracy of 93. 05%, 70. 15% and 67. 71% on CIFAR-10, CIFAR-100 and ImageNet, respectively using VGG16, with just 1 timestep.
Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks
Moreover, they require 8-14X lesser compute energy compared to their unpruned standard deep learning counterparts.
DCT-SNN: Using DCT To Distribute Spatial Information Over Time for Low-Latency Spiking Neural Networks
Notably, DCT-SNN performs inference with 2-14X reduced latency compared to other state-of-the-art SNNs, while achieving comparable accuracy to their standard deep learning counterparts.
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks
Notably, DCT-SNN performs inference with 2-14X reduced latency compared to other state-of-the-art SNNs, while achieving comparable accuracy to their standard deep learning counterparts.
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense
Additionally, we have also tested this approach on an in-house dataset to validate its robustness.
Towards Understanding the Effect of Leak in Spiking Neural Networks
While the leaky models have been argued as more bioplausible, a comparative analysis between models with and without leak from a purely computational point of view demands attention.
Unsupervised Abnormality Detection Using Heterogeneous Autonomous Systems
To that effect, in this paper, a heterogeneous system is proposed which estimates the degree of abnormality of an unmanned surveillance drone, analyzing real-time image and IMU (Inertial Measurement Unit) sensor data in an unsupervised manner.
