[ICCV'19] Code released (https://boqinggong.info/publications.html) for harnessing the potential of simulation for the semantic segmentation of real-world self-driving scenes by using:
Domain generalization: https://arxiv.org/abs/1909.00889,
Domain adaptation: https://arxiv.org/abs/1908.09547,
Thanks to Xiangyu Yue, Yang, and Qing.
Domain generalization: https://arxiv.org/abs/1909.00889,
Domain adaptation: https://arxiv.org/abs/1908.09547,
Thanks to Xiangyu Yue, Yang, and Qing.
boqinggong.info
Publications - Boqing Gong
top paper
Efficient Optimal Planning in non-FIFO Time-Dependent Flow Fields. https://arxiv.org/abs/1909.02198
Efficient Optimal Planning in non-FIFO Time-Dependent Flow Fields. https://arxiv.org/abs/1909.02198
arXiv.org
Efficient Optimal Planning in non-FIFO Time-Dependent Flow Fields
We propose an algorithm for solving the time-dependent shortest path problem
in flow fields where the FIFO (first-in-first-out) assumption is violated. This
problem variant is important for...
in flow fields where the FIFO (first-in-first-out) assumption is violated. This
problem variant is important for...
top paper
Detecting Deep Neural Network Defects with Data Flow Analysis. https://arxiv.org/abs/1909.02190
Detecting Deep Neural Network Defects with Data Flow Analysis. https://arxiv.org/abs/1909.02190
arXiv.org
Detecting Deep Neural Network Defects with Data Flow Analysis
Deep neural networks (DNNs) are shown to be promising solutions in many
challenging artificial intelligence tasks. However, it is very hard to figure
out whether the low precision of a DNN model...
challenging artificial intelligence tasks. However, it is very hard to figure
out whether the low precision of a DNN model...
Poly-GAN: Multi-Conditioned GAN for Fashion Synthesis. https://arxiv.org/abs/1909.02165
Check the final ICCV'19 program here: https://iccv2019.thecvf.com/
It has been a pleasure (and a lot of work) to serve as program chair along with Pr. Svetlana Lazebnik, Pr. Ming-Hsuan Yang and Pr. In So Kweon for the
IEEE/CVF International Conference in #computervision (ICCV'19)
- 4350 full submissions (twice the number of ICCV'17)
- 175 ACs, 1,500 reviewers,
- 13,000 reviews
4 award papers (11 nominations), 200 orals, 850 posters, 25 complaints.
See you in Seoul.
#computervision, #patternrecognition, #artificialintelligence, #machinelearning, #deeplearning
It has been a pleasure (and a lot of work) to serve as program chair along with Pr. Svetlana Lazebnik, Pr. Ming-Hsuan Yang and Pr. In So Kweon for the
IEEE/CVF International Conference in #computervision (ICCV'19)
- 4350 full submissions (twice the number of ICCV'17)
- 175 ACs, 1,500 reviewers,
- 13,000 reviews
4 award papers (11 nominations), 200 orals, 850 posters, 25 complaints.
See you in Seoul.
#computervision, #patternrecognition, #artificialintelligence, #machinelearning, #deeplearning
Here's a list of all the RL papers accepted to NeurIPS 2019!
https://www.endtoend.ai/blog/neurips2019-rl/
https://www.endtoend.ai/blog/neurips2019-rl/
5 Major open problems in NLP
https://deeps.site/blog/2019/09/09/nlp-problems/
Have compiled 5 major problems/opportunities for students, researchers and NLP enthusiasts to work on with open pointers to resources.
https://deeps.site/blog/2019/09/09/nlp-problems/
Have compiled 5 major problems/opportunities for students, researchers and NLP enthusiasts to work on with open pointers to resources.
deeps.site
5 Open problems in NLP
Space to uncover things that tick.
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch
Adam Stooke and Pieter Abbeel : https://arxiv.org/abs/1909.01500
#DeepLearning #PyTorch #ReinforcementLearning
Adam Stooke and Pieter Abbeel : https://arxiv.org/abs/1909.01500
#DeepLearning #PyTorch #ReinforcementLearning
arXiv.org
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch
Since the recent advent of deep reinforcement learning for game play and simulated robotic control, a multitude of new algorithms have flourished. Most are model-free algorithms which can be...
AI-powered banana diseases and pest detection
Paper: https://plantmethods.biomedcentral.com/articles/10.1186/s13007-019-0475-z
These researchers have developed a tool to tackle these silent killers with an app that can scan banana plants for early signs of infection, and alert farmers before it takes hold on their crops.
Paper: https://plantmethods.biomedcentral.com/articles/10.1186/s13007-019-0475-z
These researchers have developed a tool to tackle these silent killers with an app that can scan banana plants for early signs of infection, and alert farmers before it takes hold on their crops.
BioMed Central
AI-powered banana diseases and pest detection - Plant Methods
Background Banana (Musa spp.) is the most popular marketable fruit crop grown all over the world, and a dominant staple food in many developing countries. Worldwide, banana production is affected by numerous diseases and pests. Novel and rapid methods for…
A mathematical model from 103 years ago predicted something that was seen for the first time today: a #black_hole.
#MachineLearning could never do that: it needs observations to model anything. This is a major weak-point of ML. Let's fix it.
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because #data in those areas is rare. Gathering data in important areas is as important a skill as building fancy neural networks.
Sadly, too often, using extreme inputs to a model is more useful: e.g. by modeling physics of levers on light objects with short levers, we then built very long levers to lift extremely heavy things. Instead, ML is better suited at modeling everyday phenomena with complex models.
#MachineLearning could never do that: it needs observations to model anything. This is a major weak-point of ML. Let's fix it.
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because #data in those areas is rare. Gathering data in important areas is as important a skill as building fancy neural networks.
Sadly, too often, using extreme inputs to a model is more useful: e.g. by modeling physics of levers on light objects with short levers, we then built very long levers to lift extremely heavy things. Instead, ML is better suited at modeling everyday phenomena with complex models.
Blake Richards: Deep Learning with Ensembles of Neocortical Microcircuits
An inspiring talk by Blake Richards at the ICLR2018 emphasizing the interaction between neuroscience and machine learning. This intersection is where great things happen.
https://goo.gl/1YCjrm
https://t.iss.one/ArtificialIntelligenceArticles
An inspiring talk by Blake Richards at the ICLR2018 emphasizing the interaction between neuroscience and machine learning. This intersection is where great things happen.
https://goo.gl/1YCjrm
https://t.iss.one/ArtificialIntelligenceArticles
YouTube
Blake Richards: Deep Learning with Ensembles of Neocortical Microcircuits (ICLR 2018 invited talks)
Abstract: Deep learning in artificial intelligence (AI) has demonstrated that learning hierarchical representations is a good approach for generating useful sensorimotor behaviors. However, the key to effective hierarchical learning is a mechanism for ""credit…
NeurIPS 2019 Stats
Blog by Diego Charrez : https://medium.com/@dcharrezt/neurips-2019-stats-c91346d31c8f
#MachineLearning #Neurips #Neurips2019 #ArtficialIntelligence
Blog by Diego Charrez : https://medium.com/@dcharrezt/neurips-2019-stats-c91346d31c8f
#MachineLearning #Neurips #Neurips2019 #ArtficialIntelligence
Medium
NeurIPS 2019 Stats
The Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS) is going to be held at the Vancouver Convention…
Google today open-sourced Coached Conversational Preference Elicitation (CCPE) and Taskmaster-1, datasets of dialog between two people. Both datasets are being shared by Google AI researchers to supply the training material necessary to model natural language systems that achieve human-level performance.
https://venturebeat.com/2019/09/06/google-open-sources-datasets-for-ai-assistants-with-human-level-understanding/
https://venturebeat.com/2019/09/06/google-open-sources-datasets-for-ai-assistants-with-human-level-understanding/
VentureBeat
Google open-sources datasets for AI assistants with human-level understanding
Google open-sourced the Coached Conversational Preference Elicitation and Taskmaster-1 datasets to model natural language systems.
Robotics in neuroscience
Robotics has many tools that can applied to neuro-prosthetics. For example, I research noninvasive neural interfaces for prosthetics control. For me focusing on learning perception (sensors), control techniques, and AI/machine was very helpful to develop control algorithms for physical devices. Signal processing is critical in my research as well.
The basics of kinematics / Dynamics is also helpful for studying human Biomechanics and analysing how the devices you design affect the users.
Check out the abstracts at this conference:
https://icorr2019.org/
It will give you an idea of what's happening at the intersection of robotics and rehab medicine.
Robotics has many tools that can applied to neuro-prosthetics. For example, I research noninvasive neural interfaces for prosthetics control. For me focusing on learning perception (sensors), control techniques, and AI/machine was very helpful to develop control algorithms for physical devices. Signal processing is critical in my research as well.
The basics of kinematics / Dynamics is also helpful for studying human Biomechanics and analysing how the devices you design affect the users.
Check out the abstracts at this conference:
https://icorr2019.org/
It will give you an idea of what's happening at the intersection of robotics and rehab medicine.
The deepest problem with deep learning
Some reflections on an accidental Twitterstorm, the future of AI and deep learning, and what happens when you confuse a schoolbus with a snow plow.
https://medium.com/@GaryMarcus/the-deepest-problem-with-deep-learning-91c5991f5695
Some reflections on an accidental Twitterstorm, the future of AI and deep learning, and what happens when you confuse a schoolbus with a snow plow.
https://medium.com/@GaryMarcus/the-deepest-problem-with-deep-learning-91c5991f5695
Medium
The deepest problem with deep learning
Some reflections on an accidental Twitterstorm, the future of AI and deep learning, and what happens when you confuse a schoolbus with a…
AutoGMM: Automatic Gaussian Mixture Modeling in Python. https://arxiv.org/abs/1909.02688
Intensity augmentation for domain transfer of whole breast segmentation in MRI. https://arxiv.org/abs/1909.02642
Deep Iterative Frame Interpolation for Full-frame Video Stabilization. https://arxiv.org/abs/1909.02641