Hi everyone !
I am looking for papers that would show that deep learning can “invent” things, especially in the domain of super-resolution/upsampling. I only find papers that boasts the merits of their method, but not papers that show that their network can mistake high frequency features and noise or stuff like that…
Anyone?
Thanks!
submitted by /u/luciolis
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For people who dont want to wait till the list comes on CVF OpenAccess or IEEE Explore, here is the list.
Oral — https://github.com/hoya012/CVPR-2019-Paper-Statistics/blob/master/2019_cvpr/cvpr_2019_oral.csv
Poster (all papers are posters) — https://github.com/hoya012/CVPR-2019-Paper-Statistics/blob/master/2019_cvpr/cvpr_2019_poster.csv
submitted by /u/skrish13
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Hi,
I’m currently employed as a Machine Learning Engineer at a startup performing Computer Vision tasks (currently Object Detection). I’ve never produced papers and don’t have the experience or time at work to do so. I’d love to understand the process of creating papers and produce them in topics I find interesting in the future. Hence to gain this experience I’d like to offer my time to any current PhD’s/post-docs/other researchers, free of charge, in return for co-authorship in papers and a learning experience for me (and probably both parties).
Qualifications: BENg + MSc (Distinction) in Engineering from top 100 world university/top 20 UK university (Russell Group).
Skills: Good understanding of a large amount of ML literature and methods (especially CNN related) and strong capacity to learn, Python, OpenCV, Pandas, Scikit-Learn, Tensorflow, Keras, AWS, WEKA
Multiple Previous Projects in: Image Classification, Object Detection, Time Series Analysis and Prediction
If needed I have a laptop with a reasonable GPU for running models too.
Please comment on this post or drop me a message with any advice you may have or opportunities to collaborate, thanks.
submitted by /u/theLastQuestionLTBL
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The grass really is greener on the AI side. Grownetics CEO and co-founder Vince Harkiewicz would know. He helps grow it.
AI isn’t new to agtech, of course. But Grownetics, an intelligent cultivation management system for indoor farms and greenhouses, has a very specific focus: cannabis.
“We’ve specifically targeted the cannabis industry because there’s a lack of tools built for them and indoor agriculture as a whole,” Harkiewicz explained in a conversation with AI Podcast host Noah Kravitz.
Grownetics handles every step of the cultivation process, Harkiewicz says. Using harvest data, an open sensor network and a deep learning recommendation engine, the company provides a “specific recipe leading to an ideal yield for that particular variety” of cannabis, or any crop.
While he’s focused cannabis, for now, Harkiewicz believes Grownetics’ work in the industry will support growth in the broader indoor agriculture market.
“I’d argue that [the cannabis industry is] leading the indoor-ag field,” Harkiewicz said. “The two industries don’t really communicate too much yet, and that’s really what we’re striving to do, is to bridge precision agriculture, indoor agriculture with what’s been going on in the cannabis space.”
Based in Boulder, Colo., Grownetics began running beta tests at the end of last year. Since then, the company has gained eight clients and hopes to commercially launch at the end of this year.
“It’s been intense,” Harkiewicz said. “Not only are we a startup, but we’re a startup in a startup industry.”
The cannabis industry has seen immense growth in recent years as multiple countries and U.S. states have legalized cannabis for medicinal and recreational use. However, for Grownetics’ operations in the U.S., the federal legal status of the crop poses an extra hurdle.
“Because of the federal legality, or illegality of the cannabis industry, it’s artificially suppressing the market and making it extremely hard for our customers to grow and scale great businesses,” Harkiewicz said.
Even with these legal challenges, Harkiewicz is optimistic about the future of the cannabis industry and its influence on agriculture.
“This is a systemic evolution that we’re looking at, from producing the unique medicinal product in cannabis, and doing it in a pharmaceutical high-quality, clean way,” Harkiewicz said.
“And then taking those same traits to leafy greens and produce to be growing them indoors without any pesticides, and very, very efficiently.”
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This tutorial demonstrates the availability of streaming data in a data science environment, which is useful for working with real-time and fresh datasets.
First, we collect data from an existing Kafka stream into an Iguazio time series table. Next, we visualize the stream with a Grafana dashboard; and finally, we access the data in a Jupyter notebook using Python code.
We use a Nuclio serverless function to “listen” to a Kafka stream and then ingest its events into our time series table. Iguazio gets you started with a template for Kafka to time series.
We visualize the data with Grafana and work with time series data using Python code in Jupyter. Data scientist easily access both historical and real-time data in a full Python environment for exploration and training with Iguazio.
Stream data into your Jupyter notebook on Iguazio’s Data Science Platform by signing up for a free trial.
Processing video 0k044bxk4r031…
submitted by /u/IguazioDani
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I have installed all the cuda things necessary for it, but why will it not work? It gives me this error:
C:UserspcDocumentsAi TestText Test AITestBench3 Rap>train.py Traceback (most recent call last): File "C:UserspcAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonpywrap_tensorflow.py", line 58, in <module> from tensorflow.python.pywrap_tensorflow_internal import * File "C:UserspcAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonpywrap_tensorflow_internal.py", line 28, in <module> _pywrap_tensorflow_internal = swig_import_helper() File "C:UserspcAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonpywrap_tensorflow_internal.py", line 24, in swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) File "C:UserspcAppDataLocalProgramsPythonPython36libimp.py", line 243, in load_module return load_dynamic(name, filename, file) File "C:UserspcAppDataLocalProgramsPythonPython36libimp.py", line 343, in load_dynamic return _load(spec) ImportError: DLL load failed: The specified module could not be found. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "C:UserspcDocumentsAi TestText Test AITestBench3 Raptrain.py", line 3, in <module> import tensorflow as tf File "C:UserspcAppDataLocalProgramsPythonPython36libsite-packagestensorflow__init__.py", line 24, in <module> from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import File "C:UserspcAppDataLocalProgramsPythonPython36libsite-packagestensorflowpython__init__.py", line 49, in <module> from tensorflow.python import pywrap_tensorflow File "C:UserspcAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonpywrap_tensorflow.py", line 74, in <module> raise ImportError(msg) ImportError: Traceback (most recent call last): File "C:UserspcAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonpywrap_tensorflow.py", line 58, in <module> from tensorflow.python.pywrap_tensorflow_internal import * File "C:UserspcAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonpywrap_tensorflow_internal.py", line 28, in <module> _pywrap_tensorflow_internal = swig_import_helper() File "C:UserspcAppDataLocalProgramsPythonPython36libsite-packagestensorflowpythonpywrap_tensorflow_internal.py", line 24, in swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) File "C:UserspcAppDataLocalProgramsPythonPython36libimp.py", line 243, in load_module return load_dynamic(name, filename, file) File "C:UserspcAppDataLocalProgramsPythonPython36libimp.py", line 343, in load_dynamic return _load(spec) ImportError: DLL load failed: The specified module could not be found. Failed to load the native TensorFlow runtime. See https://www.tensorflow.org/install/errors for some common reasons and solutions. Include the entire stack trace above this error message when asking for help. Thank you
submitted by /u/YeeOfficer
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![[R] Zero-shot Knowledge Transfer via Adversarial Belief Matching](https://b.thumbs.redditmedia.com/b3oas6EAwXd5hg1ut8_aGzvxqHjNjYQiZYs7zfhRHjo.jpg "[R] Zero-shot Knowledge Transfer via Adversarial Belief Matching") |
Processing gif w2ldw0o30r031… TLDR: Our task is to compress a large neural network (teacher) into a smaller one (student), but we assume that the data used to train the teacher is not available anymore. Our solution is to generate pseudo points adversarially (yellow markers above) and use those to match the student (right) to the teacher (left). Paper (with PyTorch code): https://arxiv.org/abs/1905.09768 submitted by /u/polo555 |
Hey guys, Looking for code and papers on real-time TSC.
Cheers
submitted by /u/cbsudux
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Edit: Succinct question: Do people experience their GPUs disappearing from nvidia-smi, and are not recoverable except by system reboot
We have several servers with four RTX 2080TI cards, used for research. We are noticing that there is a tendency for one GPU to unexpectedly “die” from time to time. They disappear from the list returned by nvidia-smi.
This happens with a frequency in the range of several times a week to several times a month and requires a server restart to get back online.
The GPU load varies, but there is no clear correlation between periods (days) of high load and GPUs dying.
Edit: The symptom is mostly clearly noticed by the frozen GPU disappearing from the list returned by nvidia-smi. If GPU number 2 dies, the list returns values for GPUs 0, 1 and 3.
By “dying” I mean that the GPU becomes unresponsive to the system, and is not “noticed” by nvidia-smi.
The servers each have 128 GB ram, and 28 CPU cores. Some users do run CPU-intensive simulations at the same time that some users run on the GPU, which could be causing the crash.
submitted by /u/wingtales
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