Hype | MachLearning

▲

▼

2019-11-15

[Research] We trained a self-balancing physics-based character to follow interactive motion capture.[reddit]/r/MachineLearning

Here's a twitter thread including a video of a ragdoll getting lots of cubes in the face: https://twitter.com/profbof/status/1194734191843454976

Here's a blog post: https://montreal.ubisoft.com/en/drecon-data-driven-responsive-control-of-physics-based-characters

Here's a paper, which will be presented at Siggraph Asia next week: https://static-wordpress.akamaized.net/montreal.ubisoft.com/wp-content/uploads/2019/11/13214229/DReCon.pdf

And here's a high level explanation of what this is all about:

Physics-based animation holds the promise of unlocking unprecedented levels of interaction, fidelity, and variety in games. The intricate interactions between a character and it’s environment can only be faithfully synthesized by respecting real physical principles. On the other hand, data-driven animation systems utilizing large amounts of motion capture data have already shown that artistic style and motion variety can be preserved even when tight constraints on responsiveness and motion control objectives are required by a game’s design.

To combine the strengths of both methods we developed DReCon, a character controller created using deep reinforcement learning. Essentially, simulated human characters learn to move around and balance from precisely controllable motion capture examples. Once trained, gamepad controlled characters can be fully simulated using physics and simultaneously directed with a high level of responsiveness at a surprisingly low runtime cost on today’s hardware.

[share]11	[ Google ]Get as Jupyter Notebook @a2c.fyi	2019-11-15
2019-11-11: A Simple Differentiable Programming Language https://arxiv.org/abs/1911.04523v1It would be interesting to define and study such optimizations and alternative approaches, perhaps in the setting of our language ‹› Automatic differentiation plays a prominent role in scientific computing and in modern machine learning, often in the context of powerful programming systems. The relation of the various embodiments of automatic differentiation to the mathematical notion of derivative is not always entirely clear---discrepancies can arise, sometimes inadvertently. In order to study automatic differentiation in such programming contexts, we define a small but expressive programming language that includes a construct for reverse-mode differentiation. We give operational and denotational semantics for this language. The operational semantics employs popular implementation techniques, while the denotational semantics employs notions of differentiation familiar from real analysis. We establish that these semantics coincide.

[share]

▲

▼

2019-11-15

[D] Thoughts about this conversation?[reddit]/r/MachineLearning

This thread is on a public forum(Twitter) between two scientists.

Person1 - [Director of #AI #research @nvidia, Bren #Professor @Caltech, Fmr Principal scientist @awscloud]

Person2 - Research Scientist at Deepmind

Both are entitled to their own opinions. Here's how the thread goes...

Person1(talking about her newly published work): DeepLearning is only good at interpolation. But applications need extrapolation that can reason about more complex scenarios than it is trained on. With current methods, accuracy degrades rapidly when complexity of test instances grows. Our new work aims to overcome this...

Person2: This tweet really downplays prior work. NTM, memory nets, Neural GPU, MANN, graph nets, and many, many other related methods also degrade gracefully. Your work looks like an important next step, but this rhetoric is unhelpful.

Person1: What you are doing is rhetoric and rude. We have mentioned all prior work in our paper. You don't want to engage in science. It is inevitable to get attacked online as a woman. #deepmind can engage in all kind of media hype that is unethical but I get attacked for stating facts. As a woman stating science, I get accused of engaging in rhetoric.

I personally feel this response by Person1 to be extremely out of the blue. Putting aside the fact that Person1 is a Director @ NVIDIA + some title at Caltech and Person2 is a scientist as well @Google, let's look at the simple conversation here. The thread started with a tweet about an interesting work. That was followed by a review directed only at the tweet being rhetoric. And it was then replied with something unimaginable. Am I the only one looking at this all confused?

Source post: https://twitter.com/AnimaAnandkumar/status/1194338388221972480

[share]143	@hardmaru	2019-11-15
Remember when stopping Hong Kongers booing the national anthem at football matches was a major priority for the authorities? Now they have much bigger problems and Hong Kongers have written their own anthem anyway [twitter] @benjaminbland No love lost for the Chinese national anthem at the Hong Kong vs Bahrain World Cup qualifier #HongKongProtestspic.twitter.com/dR8KeKhQfh 4:17 AM - 14 Nov 2019

[share]460	@fchollet	2019-11-15
The reason why Keras exists is to empower our users to solve big problems & build important applications that make a difference in the world. This is why we make it. I have the greatest respect for all of you the creators who use our tools to advance science and technology.

[share]

▲

▼

2019-11-15

School shootings by guns owned per capita per state data set[reddit]/r/datasets

After today’s shooting in Santa Clarita, I’d like to get a holistic view of school shootings (no other type of shooting) by state in the US

[share]

▲

▼

[ Facebook ]Get as Jupyter Notebook @a2c.fyi

2019-11-14

[R] Momentum Contrast for Unsupervised Visual Representation Learning: "the gap between unsupervised and supervised representation learning has been largely closed"[reddit]https://arxiv.org/abs/1911.05722/r/MachineLearningBeyond the simple instance discrimination task [59], it is possible to adopt MoCo for pretext tasks like masked auto-encoding, e.g., in language [10] and in vision [44]

‹

Figure 1. Momentum Contrast (MoCo) trains a visual representation encoder by matching an encoded query q to a dictionary of encoded keys using a contrastive loss. The dictionary keys {k0, k1, k2, ...} are defined on-the-fly by a set of data samples. The dictionary is built as a queue, with the current mini-batch enqueued and the oldest mini-batch dequeued, decoupling it from the mini-batch size. The keys are encoded by a slowly progressing encoder, driven by a momentum update with the query encoder. This method enables a large and consistent dictionary for learning visual representations. (Introduction)

Figure 2. Conceptual comparison of three contrastive loss mechanisms (empirical comparisons are in Figure ?? and Table ??). Here we illustrate one pair of query and key. The three mechanisms differ in how the keys are maintained and how the key encoder is updated. (a): The encoders for computing the query and key representations are updated end-to-end by back-propagation (the two encoders can be different). (b): The key representations are sampled from a memory bank [59]. (c): MoCo encodes the new keys on-the-fly by a momentum-updated encoder, and maintains a queue (not illustrated in this figure) of keys. (Method)

Figure 3. Comparison of three contrastive loss mechanisms under the ImageNet linear classification protocol. We adopt the same pretext task (Sec. ??) and only vary the contrastive loss mechanism (Figure 2). The number of negatives is K in memory bank and MoCo, and is K−1 in end-to-end (offset by one because the positive key is in the same mini-batch). The network is ResNet-50. (Linear Classification Protocol)

› Abstract We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning [27] as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks.

[share]

▲

▼

2019-11-14

This video explains exactly how convolutional neural networks work, with a cool implementation. The code is written in Python and implemented with Keras.[reddit][youtube.com]/r/MLQuestions

https://www.redditmedia.com/mediaembed/dw89rl

[share]

▲

▼

[ USydne ]Get as Jupyter Notebook @a2c.fyi

2019-11-14

Fusion in a magnetically-shielded-grid inertial electrostatic confinement device https://arxiv.org/abs/1510.01788Our results constrast with earlier work by Rider [33], whose conclusions we regard as dubious, and which cannot be recovered unless we assume a Maxwellian, quasineutral plasma with the density distribution due to Krall (derived for non-Maxwellian distribution) and neglect the use of the electrostatic potential for the mitigation of cusp losses

‹

FIG. 1: A schematic diagram of a spherical gridded IEC device, where the inner electrode is the cathode. (Introduction)

FIG. 2: Two-ring cathode grid with embedded magnetic field coils that produce a magnetic cusp. Magnetic field lines are in black; electric potential is shown as a heatmap with blue representing a negative potential and red representing a ground (a grounded cylindrical chamber is implicit at the edges of the diagram) (Derivation of energy gain and loss expressions)

FIG. 3: Example Vacuum electric potential of charged cusp. (Density profiles and fusion power)

FIG. 4: Electric potential of example system with plasma present. (Density profiles and fusion power)

FIG. 5: Vacuum electric potential of electron-free virtualcathode configuration (Results)

FIG. 6: Electric potential of virtual-cathode configuration with ions present (Results)

FIG. 7: Electric potential along a radial line through the midpoint of the device for electron-free configuration. (Results)

FIG. 8: Electric potential along an axial line through centre the device for electron-free configuration. (Results)

FIG. 9: 3D slice of ion density for the electron-free virtualcathode configuration (Results)

FIG. 10: Ion density in integrated (r,z) space for electronfree virtual-cathode configuration. Black circles show the coil cross-section. (Results)

FIG. 11: Fusion-rate density in integrated (r,z) space for electron-free virtual-cathode configuration. Black circles show the coil cross-section; most of the fusion is occuring on the axis near the planes of the coils. (Results)

FIG. 12: Vacuum electric potential of electron/ion system (Results)

FIG. 13: Electric potential of electron/ion system with plasma present (Results)

FIG. 14: Electric potential along a radial line through the midpoint of the device where electrons are present. (Results)

FIG. 15: Electric potential along an axial line through centre the device where electrons are present. (Results)

FIG. 16: Ion density in the electron/ion system (Results)

FIG. 17: Electron density in the electron/ion system (Results)

FIG. 18: Fusion rate density in the electron/ion system (Results)

› \begin{abstract} Theory for a gridded inertial electrostatic confinement (IEC) fusion system is presented that shows a net energy gain is possible if the grid is magnetically shielded from ion impact. A simplified grid geometry is studied, consisting of two negatively-biased coaxial current-carrying rings, oriented such that their opposing magnetic fields produce a spindle cusp. Our analysis indicates that better than break-even performance is possible even in a deuterium-deuterium system at bench-top scales. The proposed device has the unusual property that it can avoid both the cusp losses of traditional magnetic fusion systems and the grid losses of traditional IEC configurations. \end{abstract}

[share]103	@hardmaru	2019-11-14
Im going to work on artificial general intelligence. John Carmack [twitter] “I’m going to work on artificial general intelligence.” – John Carmack https://twitter.com/hn_frontpage/status/1194757043879591942 …pic.twitter.com/6tuk78hWvd 9:15 PM - 13 Nov 2019