Archives for representation learning
ELECTRA is the present state-of-the-art in GLUE and SQuAD benchmarks. It is a self-supervised language representation learning model
The post How ELECTRA outperforms RoBERTa, ALBERT and XLNet appeared first on Analytics India Magazine.
ODE2VAE, a representation learning model, achieves state-of-the-art performance in long-term motion prediction and imputation tasks
The post Guide to ODE2VAE For Long-Term Motion Prediction appeared first on Analytics India Magazine.
“Twitter perhaps one of the largest producers of graph-structured data in the world, second only to the Large Hadron Collider!” Graphs are popular with fields like biology, quantum chemistry, and high-energy physics. Social media platforms like Twitter too, are leveraging graph-based ML for their services. Before we get into how social media platforms can benefit…
The post Why Community Platforms Should be Built On GraphML appeared first on Analytics India Magazine.
The guiding principle of generative models is being able to construct a convincing example of the data that it is fed with. The more realistic the image, the stronger is the evidence that the model has grasped the objective. Generative models offer an appealing alternative to self-supervised tasks in that they are trained to model…
The post BigBiGAN Sets New Benchmark For Unsupervised Representation Learning appeared first on Analytics India Magazine.
The International Conference on Learning Representations (ICLR) is one of the highly regarded deep learning conferences conducted every year at the end of spring. The 2019 edition witnessed over fifteen hundred submissions of which 524 papers were accepted. ICLR considers a variety of topics for the conference, such as: Feature learning Metric learning Compositional modelling…
The post 7 Most Exciting Deep Learning Papers Presented At ICLR 2019 appeared first on Analytics India Magazine.
Machine Learning models suffer from a lack of structured data. The representations of the data are sometimes entangled. This is where prior knowledge of humans comes in handy to perform feature engineering. In order to make the algorithms to exploit the inherent representations within data, disentanglement methods were introduced. There is no single definition of…
The post Debunking Widely Held Assumptions In Unsupervised Learning Of Disentangled Representations appeared first on Analytics India Magazine.