Just want to take the temperature of the community at this point in time. What are you using for research and production right now, tensorflow or pytorch? What is your experiences with speed of development for writing papers, code quality and eventual release to a production environment?

And if you want to elaborate what more do you use in conjunction, git for versioning, tensorboard for monitoring and docker for environment or something completely different?

Personally I’ve been using pytorch for smaller experiments and my local environment but will start doing larger experiments and want to learn from you all.

https://github.com/benedekrozemberczki/awesome-decision-tree-papers

A curated list of decision, classification and regression tree research papers with implementations from the following conferences.

Machine learning:

1. NeurIPS
2. ICML
3. ICLR

Computer vision:

1. CVPR
2. ICCV
3. ECCV

Natural language processing:

1. ACL
2. NAACL
3. EMNLP

Data Mining:

1. KDD
2. ICDM
3. CIKM
4. WWW

Artificial intelligence:

1. AAAI
2. IJCAI
3. UAI
4. AISTATS

Video – https://youtu.be/ynMYVdb7mO8

It was always my little dream to do exactly this project. I know people have done it before, but being a beginner in both Unity and Reinforcement Learning I thought I share the results of my little dream 😛

​

The agent taught itself how to land, no human taught it or shown how to do it. Trained for 500k steps (ca. 1h), could have ended sooner – image

​

Maximum reward/score is 20, its almost impossible to reach. It would mean 0 landing speed + 0 horizontal offset from the target. Needless to say, humans have no chance to land like that 😛

Reward function – click me

​

Will retrain with different parameters. Right now batch_size: 10 and buffer_size: 100, rest default.

​

Todo:

– Add fuel consumption

– Lessen the thrust of sideways engines

– Simulate something more similar to a SpaceX landing, where a rocket falls for a period of time before landing

– Add human controlled rocket for competitive play

– Host the game on the web

– ???

​

ps. Elon Musk, hire me! 😀

SpecAugment experiments using tensor2tensor

​

https://github.com/Kyubyong/specAugment

​

This is an implementation of SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition.

Notes

• The paper introduces three techniques for augmenting speech data in speech recognition.
• They come from the observation that spectrograms which often used as input can be treated as images, so various image augmentation methods can be applied.
• I find the idea interesting.
• It covers three methods: time warping, frequency masking, and time masking.
• Details are clearly explained in the paper.
• While the first one, time warping, looks salient apparently, Daniel, the first author, told me that indeed the other two are much more important than time warping, so it can be ignored if necessary. (Thanks for the advice, Daniel!)
• I found that implementing time warping with TensorFlow is tricky because the relevant functions are based on the static shape of the melspectrogram tensor, which is hard to get from the pre-defined graph.
• I test frequency / time masking on Tensor2tensor’s LibriSpeech Clean Small Task.
• The paper used the LAS model, but I stick to Transformer.
• To compare the effect of specAugment, I also run a base model without augmentation.

In the following tutorial, author collected data into R via an Amazon Redshift cluster in Panoply data warehouse, visualized it, created predictive models, and tested their accuracy: Connecting R To A Redshift Data Warehouse For Machine Learning

Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals

Abstract: Insects and hummingbirds exhibit extraordinary flight capabilities and can simultaneously master seemingly conflicting goals: stable hovering and aggressive maneuvering, unmatched by small scale man-made vehicles. Flapping Wing Micro Air Vehicles (FWMAVs) hold great promise for closing this performance gap. However, design and control of such systems remain challenging due to various constraints. Here, we present an open source high fidelity dynamic simulation for FWMAVs to serve as a testbed for the design, optimization and flight control of FWMAVs. For simulation validation, we recreated the hummingbird-scale robot developed in our lab in the simulation. System identification was performed to obtain the model parameters. The force generation, open-loop and closed-loop dynamic response between simulated and experimental flights were compared and validated. The unsteady aerodynamics and the highly nonlinear flight dynamics present challenging control problems for conventional and learning control algorithms such as Reinforcement Learning. The interface of the simulation is fully compatible with OpenAI Gym environment. As a benchmark study, we present a linear controller for hovering stabilization and a Deep Reinforcement Learning control policy for goal-directed maneuvering. Finally, we demonstrate direct simulation-to-real transfer of both control policies onto the physical robot, further demonstrating the fidelity of the simulation.

Page with more information and video demonstrations: https://www.purdue.edu/newsroom/releases/2019/Q2/hummingbird-robot-uses-ai-to-soon-go-where-drones-cant.html

Paper: https://arxiv.org/abs/1902.09628

Code for ICRA 2019: https://github.com/purdue-biorobotics/flappy

This AI’s contemporary prose is so rich in imagery, and allegory.

My Randomly thought of Prompt:

It was a quaint village, not too far from the southern bay. The smell of grass fumed — like it always did early spring — as the sound of the stream rushed along the riverside. A girl stood and wondered while taking in the cool crisp air. These were her final moments.

GPT Model 345M Output:

No sooner had she placed her hand on the nameplate on the plaque than a shaft of sunlight stabbed through the mist at her feet. She looked down at the cold marble inside and saw that underneath was her sister’s diamond-studded slipcase. It was open for inspection. It was ice cold and dark and strangely, she called it “Shiny.” She was so lonely at this point it hurt badly. The girl let out a short struggle. Snow drifts filled the sky, overlapping like sides of frost inside the ladder from which she slid down into the fissure of the mountain. At last, a firm hand fell on the hard smooth stone again. It’s dull, granite surface gleamed out in the dark wind beneath, like a mouth of millions of teeth. “You’ll remember me for the court case we win today. Then the dust will pass.” She looked down at her carved stone and over at the child. “They’ll see right through it.” There was the crossroads, and no spiritual journey was made.

When she finally got down, as far as she had ever gone on ice — still heading down on the roads — there was hardly a breath of wind. The little snow from the bottom of the mountain director faced directly up, overhead, as her face opened up into sincere smiling and the cold slab at her heel curled around her into an image of life. As she let out her first breath of air she clawed like a red-clawed beast at the chain that tightens at the base of her glassy skull and when she broke the tight clasp and looked up into the direction of the softly glowing sun, it felt like seeing snow landscape like a panel cast from crystalline stone. It wasn’t snow on her foot — for thousands of years, snow had fallen on virtually every other person away from religion — and the snow was real after all, shadowed and much too complex to fit into a myth. But, and her certainest prayer in her fingers, the joy still lingered when she saw her moon face, minus a skull to juxtapose the lineaments of our mortal soul.