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NF-Nets: Normalizer Free Nets

The beginning of the downfall for Batch Normalization? Introduction DeepMind released a new family of state-of-the-art networks for Image Classification that has surpassed the previous best - EfficientNet - by quite a margin. The main idea behind the new architecture is the use of Normalizer Free Neural Nets or NF-Nets to train networks instead of … Continue reading NF-Nets: Normalizer Free Nets

Interactive Video Stylization Using Few-Shot Patch-Based Training

Style transfer is an interesting problem in machine learning research where we have two input images, one for content and another for style, and the output is our content image re-imagined with this new style. The content can be a photo straight from our camera, and the style can be a painting, which leads to … Continue reading Interactive Video Stylization Using Few-Shot Patch-Based Training

Ensemble Learning in ML – Part 3: Stacking

Introduction In the previous posts, we discussed Bagging and Boosting ensemble learning in ML and how they are useful. We also discussed the algorithms which are based on it i.e. Ada Boost and Gradient Boosting. In this part, we will discuss another ensemble learning technique known as Stacking. We also discuss a bit about Blending … Continue reading Ensemble Learning in ML – Part 3: Stacking

Ensemble Learning in ML – Part 2: Boosting

Introduction In the last part, we discussed what ensembling learning in ML is and how it is useful. We also discussed one ensemble learning technique - Bagging - and algorithms that are based on it i.e. Bagging meta-estimator and Random Forest. In this part, we will discuss another ensemble learning technique, which is known as … Continue reading Ensemble Learning in ML – Part 2: Boosting

Ensemble Learning in ML – Part 1: Bagging

Introduction  Let’s understand ensemble learning with an example. Suppose you have a startup idea and you wanted to know whether that idea is good to move ahead with it or not. Now, you want to take preliminary feedback on it before committing money and your precious time to it. So you may ask one of … Continue reading Ensemble Learning in ML – Part 1: Bagging

Workflow and Implications of membrane lipids

Introduction Membrane lipids play diverse roles in cellular function. On the membrane, they act as structural elements, serve as a pool of secondary messengers and act as a platform for membrane proteins.  Phosphatidyinositol (PI) plays an important role in signal transduction and membrane trafficking. The PI on the plasma membrane is phosphorylated to P1 and … Continue reading Workflow and Implications of membrane lipids

Discovery of novel molecular pathways linked to Insulin Resistance

Introduction Insulin resistance (IR) is a clinical and major pathological condition that occurs due to inappropriate cell response to insulin hormone and abnormal secretion in the body. The decrease in insulin sensitivity leads to the progression of many metabolic disorders such as auto-immune diseases, type-1 diabetes mellitus (T1DM), obesity, atherosclerosis, cardiovascular diseases, etc. In some … Continue reading Discovery of novel molecular pathways linked to Insulin Resistance

GATT in Bluetooth Low Energy (BLE)

What is GATT in BLE? To use Bluetooth technology for communication, a device must obey the subset of Bluetooth profiles necessary to use the desired services. A Bluetooth profile is a specification regarding an aspect of Bluetooth-based wireless communication between devices. It resides on top of the Bluetooth Core Specification protocol. Bluetooth profiles are simply … Continue reading GATT in Bluetooth Low Energy (BLE)

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

Today, a variety of techniques exist that can take an image that contains humans and perform pose estimation on it. This gives us interesting skeletons that show us the current posture of the subjects shown in the given images. Having a skeleton opens up the possibility for many cool applications, for instance, it’s great for … Continue reading PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

CLEVRER: CoLlision Events for Video REpresentation and Reasoning

I recently came across a paper "CLEVRER" ("CoLlision Events for Video REpresentation and Reasoning", by - Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, Joshua B. Tenenbaum). It intrigued me, so I wanted to share some thoughts about it. With the advancements in NN-based learning algorithms, many of us are wondering … Continue reading CLEVRER: CoLlision Events for Video REpresentation and Reasoning