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This paper tests the following hypothesis, about features learned by a deep network trained on the ImageNet dataset: *Object features and anticausal features are closely related. Context features and causal features are not necessarily related.* First, some definitions. Let $X$ be a visual feature (i.e. value of a hidden unit) and $Y$ be information about a label (e.g. the logodds of probability of different object appearing in the image). A causal feature would be one for which the causal direction is $X \rightarrow Y$. An anticausal feature would be the opposite case, $X \leftarrow Y$. As for object features, in this paper they are features whose value tends to change a lot when computed on a complete original image versus when computed on an image whose regions *falling inside* object bounding boxes have been blacked out (see Figure 4). Contextual features are the opposite, i.e. values change a lot when blacking out the regions *outside* object bounding boxes. See section 4.2.1 for how "object scores" and "context scores" are computed following this description, to quantitatively measure to what extent a feature is an "object feature" or a "context feature". Thus, the paper investigates whether 1) for object features, their relationship with object appearance information is anticausal (i.e. whether the object feature's value seems to be caused by the presence of the object) and whether 2) context features are not clearly causal or anticausal. To perform this investigation, the paper first proposes a generic neural network model (dubbed the Neural Causation Coefficient architecture or NCC) to predict a score of whether the relationship between an input variable $X$ and target variable $Y$ is causal. This model is trained by taking as input datasets of $X$ and $Y$ pairs synthetically generated in such a way that we know whether $X$ caused $Y$ or the opposite. The NCC architecture first embeds each individual $X$,$Y$ instance pair into some hidden representation, performs mean pooling of these representations and then feeds the result to fully connected layers (see Figure 3). The paper shows that the proposed NCC model actually achieves SOTA performance on the Tübingen dataset, a collection of realworld causeeffect observational samples. Then, the proposed NCC model is used to measure the average object score of features of a deep residual CNN identified as being most causal and most anticausal by NCC. The same is done with the context score. What is found is that indeed, the object score is always higher for the top anticausal features than for the top causal features. However, for the context score, no such clear trend is observed (see Figure 5). **My two cents** I haven't been following the growing literature on machine learning for causal inference, so it was a real pleasure to read this paper and catch up a little bit on that. Just for that I would recommend the reading of this paper. The paper does a really good job at explaining the notion of *observational causal inference*, which in short builds on the observation that if we assume IID noise on top of a causal (or anticausal) phenomenon, then causation can possibly be inferred by verifying in which direction of causation the IID assumption on the noise seems to hold best (see Figure 2 for a nice illustration, where in (a) the noise is clearly IID, but isn't in (b)). Also, irrespective of the study of causal phenomenon in images, the NCC architecture, which achieves SOTA causal prediction performance, is in itself a nice contribution. Regarding the application to image features, one thing that is hard to wrap your head around is that, for the $Y$ variable, instead of using the true image label, the logodds at the output layer are used instead in the study. The paper justifies this choice by highlighting that the NCC network was trained on examples where $Y$ is continuous, not discrete. On one hand, that justification makes sense. On the other, this is odd since the logodds were in fact computed directly from the visual features, meaning that technically the value of the logodds are directly caused by all the features (which goes against the hypothesis being tested). My best guess is that this isn't an issue only because NCC makes a causal prediction between *a single feature* and $Y$, not *from all features* to $Y$. I'd be curious to read the authors' perspective on this. Still, this paper at this point is certainly just scratching the surface on this topic. For instance, the paper mentions that NCC could be used to encourage the learning of causal or anticausal features, providing a new and intriguing type of regularization. This sounds like a very interesting future direction for research, which I'm looking forward to.
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This paper argues that, in semisupervised learning, it's suboptimal to use the same weight for all examples (as happens implicitly, when the unsupervised component of the loss for each example is just added together directly. Instead, it tries to learn weights for each specific data example, through a metalearningesque process. The form of semisupervised learning being discussed here is labelbased consistency loss, where a labeled image is augmented and run through the current version of the model, and the model is optimized to try to induce the same loss for the augmented image as the unaugmented one. The premise of the authors argument for learning perexample weights is that, ideally, you would enforce consistency loss less on examples where a model was unconfident in its label prediction for an unlabeled example. As a way to solve this, the authors suggest learning a vector of parameters  one for each example in the dataset  where element i in the vector is a weight for element i of the dataset, in the summedup unsupervised loss. They do this via a twostep process, where first they optimize the parameters of the network given the example weights, and then the optimize the example weights themselves. To optimize example weights, they calculate a gradient of those weights on the posttraining validation loss, which requires backpropogating through the optimization process (to determine how different weights might have produced a different gradient, which might in turn have produced better validation loss). This requires calculating the inverse Hessian (second derivative matrix of the loss), which is, generally speaking, a quite costly operation for hugeparameter nets. To lessen this cost, they pretend that only the final layer of weights in the network are being optimized, and so only calculate the Hessian with respect to those weights. They also try to minimize cost by only updating the example weights for the examples that were used during the previous update step, since, presumably those were the only ones we have enough information to upweight or downweight. With this model, the authors achieve modest improvements  performance comparable to or withinerrorbounds better than the current state of the art, FixMatch. Overall, I find this paper a little baffling. It's just a crazy amount of effort to throw into something that is a minor improvement. A few issues I have with the approach:  They don't seem to have benchmarked against the simpler baseline of some inverse of using Dropoutestimated uncertainty as the weight on examples, which would, presumably, more directly capture the property of "is my model unsure of its prediction on this unlabeled example"  If the presumed need for this is the lack of certainty of the model, that's a nonstationary problem that's going to change throughout the course of training, and so I'd worry that you're basically taking steps in the direction of a moving target  Despite using techniques rooted in metalearning, it doesn't seem like this models learns anything generalizable  it's learning indexbased weights on specific examples, which doesn't give it anything useful it can do with some new data point it finds that it wasn't specifically trained on Given that, I think I'd need to see a much stronger case for dramatic performance benefits for something like this to seem like it was worth the increase in complexity (not to mention computation, even with the optimized Hessian scheme) 
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This paper deals with the question what / how exactly CNNs learn, considering the fact that they usually have more trainable parameters than data points on which they are trained. When the authors write "deep neural networks", they are talking about Inception V3, AlexNet and MLPs. ## Key contributions * Deep neural networks easily fit random labels (achieving a training error of 0 and a test error which is just randomly guessing labels as expected). $\Rightarrow$Those architectures can simply bruteforce memorize the training data. * Deep neural networks fit random images (e.g. Gaussian noise) with 0 training error. The authors conclude that VCdimension / Rademacher complexity, and uniform stability are bad explanations for generalization capabilities of neural networks * The authors give a construction for a 2layer network with $p = 2n+d$ parameters  where $n$ is the number of samples and $d$ is the dimension of each sample  which can easily fit any labeling. (Finite sample expressivity). See section 4. ## What I learned * Any measure $m$ of the generalization capability of classifiers $H$ should take the percentage of corrupted labels ($p_c \in [0, 1]$, where $p_c =0$ is a perfect labeling and $p_c=1$ is totally random) into account: If $p_c = 1$, then $m()$ should be 0, too, as it is impossible to learn something meaningful with totally random labels. * We seem to have built models which work well on image data in general, but not "natural" / meaningful images as we thought. ## Funny > deep neural nets remain mysterious for many reasons > Note that this is not exactly simple as the kernel matrix requires 30GB to store in memory. Nonetheless, this system can be solved in under 3 minutes in on a commodity workstation with 24 cores and 256 GB of RAM with a conventional LAPACK call. ## See also * [Deep Nets Don't Learn Via Memorization](https://openreview.net/pdf?id=rJv6ZgHYg) 
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The authors propose a unified setting to evaluate the performance of batch reinforcement learning algorithms. The proposed benchmark is discrete and based on the popular Atari Domain. The authors review and benchmark several current batch RL algorithms against a newly introduced version of BCQ (Batch Constrained Deep Q Learning) for discrete environments. https://i.imgur.com/zrCZ173.png Note in line 5 that the policy chooses actions with a restricted argmax operation, eliminating actions that have not enough support in the batch. One of the key difficulties in batchRL is the divergence of value estimates. In this paper the authors use Double DQN, which means actions are selected with a value net $Q_{\theta}$ and the policy evaluation is done with a target network $Q_{\theta'}$ (line 6). **How is the batch created?** A partially trained DQNagent (trained online for 10mio steps, aka 40mio frames) is used as behavioral policy to collect a batch $B$ containing 10mio transitions. The DQN agent uses either with probability 0.8 an $\epsilon=0.2$ and with probability 0.2 an $\epsilon = 0.001$. The batch RL agents are trained on this batch for 10mio steps and evaluated every 50k time steps for 10 episodes. This process of batch creation differs from the settings used in other papers in i) having only a single behavioral policy, ii) the batch size and iii) the proficiency level of the batch policy. The experiments, performed on the arcade learning environment include DQN, REM, QRDQN, KLControl, BCQ, OnlineDQN and Behavioral Cloning and show that:  for conventional RL algorithms distributional algorithms (QRDQN) outperform the plain algorithms (DQN)  batch RL algorithms perform better than conventional algorithms with BCQ outperforming every other algorithm in every tested game In addition to the return the authors plot the value estimates for the Qnetworks. A drop in performance corresponds in all cases to a divergence (up or down) in value estimates. The paper is an important contribution to the debate about what is the right setting to evaluate batch RL algorithms. It remains however to be seen if the proposed choice of i) a single behavior policy, ii) the batch size and iii) quality level of the behavior policy will be accepted as standard. Further work is in any case required to decide upon a benchmark for continuous domains. 
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This summary builds substantially on my summary of NERFs, so if you haven't yet read that, I recommend doing so first! The idea of a NERF is learn a neural network that represents a 3D scene, and from which you can, once the model is trained, sample an image of that scene from any desired angle. This involves structuring your neural network as a function that predicts the RGB color and density/opacity for a given point in 3D space (x, y, z), from a given viewing angle (theta, phi). With such a function, you can generate predictions of what images taken from certain angles would look like by sampling along a viewing ray, and integrating the combined hue and opacity into an aggregated view. This prediction can then be compared to a true image taken from that direction, and gradients passed backwards into the prediction model. An important assumption of this model is that the scene being photographed is static; specifically, that every point in space is always inhabited by the same part of the 3D object, regardless of what angle it's viewed from. This is a reasonable assumption for photos of inanimate objects, or of humans in highly controlled lab settings, but it is often not true for humans when you, say, ask them to take a selfie video of themselves. Even if they're trying to keep roughly still, there will be slight shifts in the location and position of their head between frames, and the authors of this paper show that this can lead to strange artifacts if you naively try to train a NERF from the images (including a particularly odd one where it hallucinates tiny copies of the image in the air surrounding the face). https://i.imgur.com/IUVh6uM.png The fix proposed by this paper is to apply a learnable deformation field to each image, where the notion is to deform each view into being in one canonical position (fixed per network, since, again, one network corresponds to a single scene). This means that, along with learning the parameters of the NERF itself, you're also learning what deformation to apply to each training image to get it into this canonical position. This is done by parametrizing the deformation in a particular way, and then having that deformation be conditioned by a latent vector that's trained similar to how you'd train an embedding (one learned vector per image example). The parametrization of the deformation is honestly a little bit over my head, given my lack of grounding in 3D modeling, but my general sense is that it applies some constraints and regularization to ensure that the learned deformations are realistic, insofar as humans are mostly rigid (one patch of skin on my forehead generally doesn't move except in concordance with the rest of my forehead), but with some possibility for elasticity (skin can stretch if I, say, smile). The authors also include an annealing scheme whereby, early in training, the model focuses on learning course (largescale) deformations, and later in training, it's allowed to also learn weights for more precise deformations. This is to hopefully match macroscale shifts before adding the noise of precise changes. This addition of a learned deformation is most of the contribution of this method: with it applied, they show that they're able to learn realistic NERFs from selfies, which they term "NERFIES". They mention a few pieces of concurrent work that try to solve the same problem of nonstatic human subjects in different ways, but I haven't had a chance to read those, so I can't really comment on how NERFIES stacks up to alternate approaches, but it appears to be as least one empirically convincing solution to the problem it's aiming at. 