Groundbreaking research enabling applied AI solutions.

deepkapha.ai conducts ground-breaking yet practical AI Research so you can build your AI Solution rapidly.

AI Research

Our mission is to build practical and groundbreaking AI Research so companies and professionals can apply to their production systems. Our goal is to provide AI Solutions by implementing our algorithms, tools and technologies.

Our researchers and engineers are dedicated to working towards this goal and they contributes relentlessly towards building practical software and algorithms. We publish our research and present at leading conferences regularly but our differentiation is in applying it directly into production systems of industry verticals such as healthcare diagnostics, manufacturing operations and more.

Research

Our mission is to build practical and groundbreaking AI Research which companies and professionals can directly apply to their production systems. Our goal is to provide AI Solutions by implemnting our algorithms, tools and technologies.

Our researchers and engineers are dedicated to working towards this goal. To do so our team contributes relentlessly towards building practical software and algorithms.

We publish our research and present at leading conferences regularly but
adopt a unique strategy by applying it directly into industry verticals.
We believe that is the only way to walk the talk!

May 24th, 2019

Towards a Neurobiological Basis of Deep Learning

The human brain constantly executes myriad decisions every day – some trivial, many complex. Decision making and learning are fundamental to human survival, and our runaway success as a dominant species. Our brain continually decides by reflecting upon past experiences, while simultaneously acquiring new knowledge with every decision. Neuromodulators – acetylcholine (ACh), noradrenaline (NA), serotonin (5-HT), dopamine (DA), and histamine (HA) – reorganize the function of local neural networks neurons and shape the emergence of global brain states such as decision making and learning. The advent of artificial neural networks, which has benefited from the remarkable success of the brain’s ability to decide and learn, is attempting to transform human society through machine-based representations that mimic patterns of biological neural activity. For example, biologically inspired convolutional neural networks (CNNs) have shown promising performance in a variety of tasks including image recognition, classification and analysis. Recent studies have adopted a more biologically-realistic compartmental structure in the design of deep learning algorithms. Here, we review subcortical structures and neuromodulatory systems that regulate contextual decision making and learning in the brain, and outline proposals towards more efficient machine-based representations for neuromodulation-aware models of deep-learning. Taken together, a comprehensive review of existing findings on the role of neuromodulators in decision making and learning processes will be essential for evidence-driven, biologically-inspired deep learning models.

May 8, 2019

DLF

Generative Model with Dynamic Linear Flow

Flow-based generative models are a family of exact log-likelihood models with tractable sampling and latent-variable inference, hence conceptually attractive for modelling complex distributions. However, flow-based models are limited by density estimation performance issues as compared to state-of-the-art autoregressive models. Autoregressive models, which also belong to the family of likelihood-based methods, however suffer from limited parallelizability. In this paper, we propose Dynamic Linear Flow (DLF), a new family of invertible transformations with partially autoregressive structure. Our method benefits from the efficient computation of flow-based methods and high density estimation performance of autoregressive methods. We demonstrate that the proposed DLF yields state-of-the-art performance on ImageNet 32×32 and 64×64 out of all flow-based methods,and is competitive with the best autoregressive model. Additionally, our model converges 10 times faster than Glow (Kingma and Dhariwal, 2018).

Links to Detailed Blog Article, Link to Paper and Github Code

May 27, 2018

Intra-thalamic and Thalamocortical Connectivity: Potential Implication for Deep Learning

Contrary to the traditional view that the thalamus acts as a passive relay station of sensory information to the cortex, a number of ex-perimental studies have demonstrated the effects of peri-geniculate and cortico-thalamic projections on the transmission of visual in- put. In the present study, we implemented a mechanistic model to facilitate the understanding of perigeniculate and corticothalamic effects on the transfer function of geniculate cells and their firing patterns. As a result, the model successfully captures some funda- mental properties of early-stage visual processing in mammalian brain. We conclude, therefore, that the thalamus is not a passive relay center and the intra-thalamic circuitry is of great importance to biological vision. In summary, intra-thalamic and thalamocortical circuitry has implications in early-stage visual processing, and could constitute a valid tool for refining information relay and compression in artificial neural networks (ANN), leading to deep learning models of higher performance.
Coming up
May 22, 2018

ARiA

Utilizing Richard’s Curve for Controlling the Non-monotonicity of the Activation Function in Deep Neural Nets

This work introduces a novel activation unit that can be efficiently employed in deep neural nets (DNNs) and performs significantly better than the traditional Rectified Linear Units (ReLU). The function developed is a two parameter version of the specialized Richard’s Curve and we call it Adaptive Richard’s Curve weighted Activation (ARiA). This function is non-monotonous, analogous to the newly introduced Swish, however allows a precise control over its non-monotonous convexity by varying the hyper-parameters. We first demonstrate the mathematical significance of the two parameter ARiA followed by its application to benchmark problems such as MNIST, CIFAR-10 and CIFAR-100, where we compare the performance with ReLU and Swish units. Our results illustrate a significantly superior performance on all these datasets, making ARiA a potential replacement for ReLU and other activations in DNNs.

Coming soon: DeepSwitch

The solutions found by the adaptive algorithms like Adam fail to generalize as well as SGD in certain scenarios even though adaptive methods usually perform well on training set. So, there is often tradeoff between testing accuracy and performance update at local optimas. Keskar et. al have showed that the adaptive methods work better in the initial portion of the training but with later portion, SGD seems to work better. The basic premise of this work is to investigate the use of fuzzy logic to extend the phenomenon to a more generic, and robust control for optimizer switching. Unlike the prior work, we also incorporate quasi-Newtonian optimizers as well as other adaptive optimizers than Adam, and work out a switching logic that maximizes the generalization accuracy while having minimal effect on training time.
Coming up

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