ali5341/scitldr-chat-format huggingface dataset:
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning from Between-class Examples for Deep Sound Recognition

Paper content:
Deep learning methods have achieved high performance in sound recognition tasks.
Deciding how to feed the training data is important for further performance improvement.
We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning).
Our strategy is to learn a discriminative feature space by recognizing the between-class sounds as between-class sounds.
We generate between-class sounds by mixing two sounds belonging to different classes with a random ratio.
We then input the mixed sound to the model and train the model to output the mixing ratio.
The advantages of BC learning are not limited only to the increase in variation of the training data; BC learning leads to an enlargement of Fisher’s criterion in the feature space and a regularization of the positional relationship among the feature distributions of the classes.
The experimental results show that BC learning improves the performance on various sound recognition networks, datasets, and data augmentation schemes, in which BC learning proves to be always beneficial.
Furthermore, we construct a new deep sound recognition network (EnvNet-v2) and train it with BC learning.
As a result, we achieved a performance surpasses the human level.

assistant: text: Proposes a method to improve the performance of a generic learning method by generating "in between class" training samples and presents the basic intuition and necessity of the proposed technique.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Not-So-Random Features

Paper content:
We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels.
Our method produces a sequence of feature maps, iteratively refining the SVM margin.
We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game.
Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.

assistant: text: The authors propose an interesting algorithm for learning the l1-SVM and the Fourier represented kernel together
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Learn via Gradient Component Corrections

Paper content:
Gradient-based meta-learning algorithms require several steps of gradient descent to adapt to newly incoming tasks.
This process becomes more costly as the number  of  samples  increases.
Moreover,  the  gradient  updates  suffer  from  several sources of noise leading to a degraded performance.
In this work,  we propose a meta-learning algorithm equipped with the GradiEnt Component COrrections, aGECCO cell for short, which generates a multiplicative corrective low-rank matrix which (after vectorization) corrects the estimated gradients.
GECCO contains a simple decoder-like network with learnable parameters, an attention module and a so-called context input parameter.
The context parameter of GECCO is updated to  generate  a  low-rank  corrective  term  for  the  network  gradients.
As  a  result, meta-learning requires only a few of gradient updates to absorb new task (often, a single update is sufficient in the few-shot scenario).
While previous approaches address this problem by altering the learning rates, factorising network parameters or directly learning feature corrections from features and/or gradients, GECCO is an off-the-shelf generator-like unit that performs element-wise gradient corrections without the need to ‘observe’ the features and/or the gradients directly.
We show that our GECCO (i) accelerates learning, (ii) performs robust corrections of the gradients corrupted by a noise, and (iii) leads to notable improvements over existing gradient-based meta-learning algorithms.

assistant: text: We propose a meta-learner to adapt quickly on multiple tasks even one step in a few-shot setting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES

Paper content:
Deep neural networks (DNNs) had great success on NLP tasks such as language modeling, machine translation and certain question answering (QA) tasks.
However, the success is limited at more knowledge intensive tasks such as QA from a big corpus.
Existing end-to-end deep QA models (Miller et al., 2016; Weston et al., 2014) need to read the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size.
This is prohibitive for practical tasks such as QA from Wikipedia, a novel, or the Web.
We propose to solve this scalability issue by using symbolic meaning representations, which can be indexed and retrieved efficiently with complexity that is independent of the text size.
More specifically, we use sequence-to-sequence models to encode knowledge symbolically and generate programs to answer questions from the encoded knowledge.
We apply our approach, called the N-Gram Machine (NGM), to the bAbI tasks (Weston et al., 2015) and a special version of them (“life-long bAbI”) which has stories of up to 10 million sentences.
Our experiments show that NGM can successfully solve both of these tasks accurately and efficiently.
Unlike fully differentiable memory models, NGM’s time complexity and answering quality are not affected by the story length.
The whole system of NGM is trained end-to-end with REINFORCE (Williams, 1992).
To avoid high variance in gradient estimation, which is typical in discrete latent variable models, we use beam search instead of sampling.
To tackle the exponentially large search space, we use a stabilized auto-encoding objective and a structure tweak procedure to iteratively reduce and refine the search space.

assistant: text: This paper presents the n-gram machine, a model that encodes sentences into simple symbolic representations which can be queried efficiently.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Orthogonal Recurrent Neural Networks with Scaled Cayley Transform

Paper content:
Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients.
Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs).
We propose a simpler and novel update scheme to maintain orthogonal recurrent weight matrices without using complex valued matrices.
This is done by parametrizing with a skew-symmetric matrix using the Cayley transform.
Such a parametrization is unable to represent matrices with negative one eigenvalues, but this limitation is overcome by scaling the recurrent weight matrix by a diagonal matrix consisting of ones and negative ones.
The proposed training scheme involves a straightforward gradient calculation and update step.
In several experiments, the proposed scaled Cayley orthogonal recurrent neural network (scoRNN) achieves superior results with fewer trainable parameters than other unitary RNNs.

assistant: text: Introduces a scheme for learning the recurrent parameter matrix in a neural network that uses the Cayley transform and a scaling weight matrix.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing

Paper content:
It is well-known that  classifiers are vulnerable to adversarial perturbations.
To defend against adversarial perturbations, various certified robustness results have been derived.
However, existing certified robustnesses are limited to top-1 predictions.
In many real-world applications, top-$k$ predictions are more relevant.
In this work, we aim to derive certified robustness for top-$k$ predictions.
In particular, our certified robustness is based on randomized smoothing, which turns any classifier to a new classifier via adding noise to an input example.
We adopt randomized smoothing because it is scalable to large-scale neural networks and applicable to any classifier.
We derive a tight robustness in $\ell_2$ norm for top-$k$ predictions  when using randomized smoothing with Gaussian noise.
We find that generalizing the certified robustness  from top-1 to top-$k$ predictions faces significant technical challenges.
We also empirically evaluate our method on CIFAR10 and ImageNet.
For example, our method can obtain an ImageNet classifier with a certified top-5 accuracy of 62.8\% when the $\ell_2$-norms of the adversarial perturbations are less than 0.5 (=127/255).
Our code is publicly available at: \url{https://github.com/jjy1994/Certify_Topk}.

assistant: text: This paper extends work on deducing a certified radius using randomized smoothing, and shows the radius at which a smoothed classifier under Gaussian perturbations is certified for the top k predictions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Prediction of Multi-Agent Interactions from Partial Observations

Paper content:
We present a method which learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents.
Our method is based on a graph-structured variational recurrent neural network, which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states.
We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.

assistant: text: This paper presents a graph neural network based architecture that is trained to locate and model the interactions of agents in an environment directly from pixels and show advantage of model for tracking tasks and forecasting agent locations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Compositional Transfer in Hierarchical Reinforcement Learning

Paper content:
The successful application of flexible, general learning algorithms to real-world robotics applications is often limited by their poor data-efficiency.
To address the challenge, domains with more than one dominant task of interest encourage the sharing of information across tasks to limit required experiment time.
To this end, we investigate compositional inductive biases in the form of hierarchical policies as a mechanism for knowledge transfer across tasks in reinforcement learning (RL).
We demonstrate that this type of hierarchy enables positive transfer while mitigating negative interference.
Furthermore, we demonstrate the benefits of additional incentives to efficiently decompose task solutions.
Our experiments show that these incentives are naturally given in multitask learning and can be easily introduced for single objectives.
We design an RL algorithm that enables stable and fast learning of structured policies and the effective reuse of both behavior components and transition data across tasks in an off-policy setting.
Finally, we evaluate our algorithm in simulated environments as well as physical robot experiments and  demonstrate substantial improvements in data data-efficiency over competitive baselines.

assistant: text: A combination of different learning techniques for acquiring structure and learning with asymmetric data, used to train an HRL policy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mapping the hyponymy relation of wordnet onto vector Spaces

Paper content:
In this paper, we investigate mapping the hyponymy relation of
 wordnet to feature vectors.
We aim to model lexical knowledge in such a way that it can be used as
  input in generic machine-learning models, such as phrase entailment
  predictors.
We propose two models.
The first one leverages an existing mapping of
  words to feature vectors (fasttext), and attempts to classify
  such vectors as within or outside of each class.
The second model is fully supervised,
  using solely wordnet as a ground truth.
It maps each concept to an
  interval or a disjunction thereof.
On the first model, we approach, but not quite attain state of the
  art performance.
The second model can achieve near-perfect accuracy.

assistant: text: We investigate mapping the hyponymy relation of wordnet to feature vectors
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Once for All: Train One Network and Specialize it for Efficient Deployment

Paper content:
We address the challenging problem of efficient deep learning model deployment, where the goal is to design neural network architectures that can fit different hardware platform constraints.
Most of the traditional approaches either manually design or use Neural Architecture Search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally expensive and unscalable.
Our key idea is to decouple model training from architecture search to save the cost.
To this end, we propose to train a once-for-all network (OFA) that supports diverse architectural settings (depth, width, kernel size, and resolution).
Given a deployment scenario, we can then quickly get a specialized sub-network by selecting from the OFA network without additional training.
To prevent interference between many sub-networks during training, we also propose a novel progressive shrinking algorithm, which can train a surprisingly large number of sub-networks ($> 10^{19}$) simultaneously.
Extensive experiments on various hardware platforms (CPU, GPU, mCPU, mGPU, FPGA accelerator) show that OFA consistently outperforms SOTA NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3) while reducing orders of magnitude GPU hours and $CO_2$ emission.
In particular, OFA achieves a new SOTA 80.0% ImageNet top1 accuracy under the mobile setting ($<$600M FLOPs).
Code and pre-trained models are released at https://github.com/mit-han-lab/once-for-all.

assistant: text: This paper tries to tackle the problem of searching best architectures for specialized resource constraint deployment scenarios with a prediction based NAS method.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Varieties of Explainable Agency

Paper content:
In this paper, I discuss some varieties of explanation that can arise
in intelligent agents.
I distinguish between process accounts, which
address the detailed decisions made during heuristic search, and
preference accounts, which clarify the ordering of alternatives
independent of how they were generated.
I also hypothesize
which types of users will appreciate which types of explanation.
In addition, I discuss three facets of multi-step decision making
-- conceptual inference, plan generation, and plan execution --
in which explanations can arise.
I also consider alternative ways
to present questions to agents and for them provide their answers.

assistant: text: Discusses different aspects of explanations, particularly in the context of sequential decision making.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Novelty Search in representational space for sample efficient exploration

Paper content:
We present a new approach for efficient exploration which leverages a low-dimensional encoding of the environment learned with a combination of model-based and model-free objectives.
Our approach uses intrinsic rewards that are based on a weighted distance of nearest neighbors in the low dimensional representational space to gauge novelty.
We then leverage these intrinsic rewards for sample-efficient exploration with planning routines in representational space.
One key element of our approach is that we perform more gradient steps in-between every environment step in order to ensure the model accuracy.
We test our approach on a number of maze tasks, as well as a control problem and show that our exploration approach is more sample-efficient compared to strong baselines.

assistant: text: We conduct exploration using intrinsic rewards that are based on a weighted distance of nearest neighbors in representational space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: In-training Matrix Factorization for Parameter-frugal Neural Machine Translation

Paper content:
In this paper, we propose the use of in-training matrix factorization to reduce the model size for neural machine translation.
Using in-training matrix factorization, parameter matrices may be decomposed into the products of smaller matrices, which can compress large machine translation architectures by vastly reducing the number of learnable parameters.
We apply in-training matrix factorization to different layers of standard neural architectures and show that in-training factorization is capable of reducing nearly 50% of learnable parameters without any associated loss in BLEU score.
Further, we find that in-training matrix factorization is especially powerful on embedding layers, providing a simple and effective method to curtail the number of parameters with minimal impact on model performance, and, at times, an increase in performance.

assistant: text: This paper proposes using matrix factorization at training time for neural machine translation, which can reduce model size and decrease training time without harming performance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Imagine Manipulation Goals for Robot Task Planning

Paper content:
Prospection is an important part of how humans come up with new task plans, but has not been explored in depth in robotics.
Predicting multiple task-level is a challenging problem that involves capturing both task semantics and continuous variability over the state of the world.
Ideally, we would combine the ability of machine learning to leverage big data for learning the semantics of a task, while using techniques from task planning to reliably generalize to new environment.
In this work, we propose a method for learning a model encoding just such a representation for task planning.
We learn a neural net that encodes the k most likely outcomes from high level actions from a given world.
Our approach creates comprehensible task plans that allow us to predict changes to the environment many time steps into the future.
We demonstrate this approach via application to a stacking task in a cluttered environment, where the robot must select between different colored blocks while avoiding obstacles, in order to perform a task.
We also show results on a simple navigation task.
Our algorithm generates realistic image and pose predictions at multiple points in a given task.

assistant: text: We describe an architecture for generating diverse hypotheses for intermediate goals during robotic manipulation tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A closer look at the approximation capabilities of neural networks

Paper content:
The universal approximation theorem, in one of its most general versions, says that if we consider only continuous activation functions σ, then a standard feedforward neural network with one hidden layer is able to approximate any continuous multivariate function f to any given approximation threshold ε, if and only if σ is non-polynomial.
In this paper, we give a direct algebraic proof of the theorem.
Furthermore we shall explicitly quantify the number of hidden units required for approximation.
Specifically, if X in R^n is compact, then a neural network with n input units, m output units, and a single hidden layer with {n+d choose d} hidden units (independent of m and ε), can uniformly approximate any polynomial function f:X -> R^m whose total degree is at most d for each of its m coordinate functions.
In the general case that f is any continuous function, we show there exists some N in O(ε^{-n}) (independent of m), such that N hidden units would suffice to approximate f.
We also show that this uniform approximation property (UAP) still holds even under seemingly strong conditions imposed on the weights.
We highlight several consequences:
(i) For any δ > 0, the UAP still holds if we restrict all non-bias weights w in the last layer to satisfy |w| < δ.
(ii) There exists some λ>0 (depending only on f and σ), such that the UAP still holds if we restrict all non-bias weights w in the first layer to satisfy |w|>λ.
(iii) If the non-bias weights in the first layer are *fixed* and randomly chosen from a suitable range, then the UAP holds with probability 1.

assistant: text: The authors derive the universal approximation property proofs algebraically and assert that the results are general to other kinds of neural networks and similar learners.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Toward Understanding Generalization of Over-parameterized Deep ReLU network trained with SGD in Student-teacher Setting

Paper content:
To analyze deep ReLU network, we adopt a student-teacher setting in which an over-parameterized student network learns from the output of a fixed teacher network of the same depth, with Stochastic Gradient Descent (SGD).
Our contributions are two-fold.
First, we prove that when the gradient is zero (or bounded above by a small constant) at every data point in training, a situation called  \emph{interpolation setting}, there exists many-to-one \emph{alignment} between student and teacher nodes in the lowest layer under mild conditions.
This suggests that generalization in unseen dataset is achievable, even the same condition often leads to zero training error.
Second, analysis of noisy recovery and training dynamics in 2-layer network shows that strong teacher nodes (with large fan-out weights) are learned first and subtle teacher nodes are left unlearned until late stage of training.
As a result, it could take a long time to converge into these small-gradient critical points.
Our analysis shows that over-parameterization plays two roles: (1) it is a necessary condition for alignment to happen at the critical points, and (2) in training dynamics, it helps student nodes cover more teacher nodes with fewer iterations.
Both improve generalization.
Experiments justify our finding.

assistant: text: This paper analyzes training dynamics and critical points of training deep ReLU network via SGD in the teacher-student setting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Training GANs with Optimism

Paper content:
We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs.
Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games.
WGANs is exactly a context of solving a zero-sum game with simultaneous no-regret dynamics.
Moreover, we show that optimistic mirror decent addresses the limit cycling problem in training WGANs.
We formally show that in the case of bi-linear zero-sum games the last iterate of OMD dynamics converges to an equilibrium, in contrast to GD dynamics which are bound to cycle.
We also portray the huge qualitative difference between GD and OMD dynamics with toy examples, even when GD is modified with many adaptations proposed in the recent literature, such as gradient penalty or momentum.
We apply OMD WGAN training to a bioinformatics problem of generating DNA sequences.
We observe that models trained with OMD achieve consistently smaller KL divergence with respect to the true underlying distribution, than models trained with GD variants.
Finally, we introduce a new algorithm, Optimistic Adam, which is an optimistic variant of Adam.
We apply it to WGAN training on CIFAR10 and observe improved performance in terms of inception score as compared to Adam.

assistant: text: This paper proposes a simple modification of standard gradient descent, claiming to improve the convergence of GANs and other minimax optimization problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Synthetic vs Real: Deep Learning on Controlled Noise

Paper content:
Performing controlled experiments on noisy data is essential in thoroughly understanding deep learning across a spectrum of noise levels.
Due to the lack of suitable datasets, previous research have only examined deep learning on controlled synthetic noise, and real-world noise has never been systematically studied in a controlled setting.
To this end, this paper establishes a benchmark of real-world noisy labels at 10 controlled noise levels.
As real-world noise possesses unique properties, to understand the difference, we conduct a large-scale study across a variety of noise levels and types, architectures, methods, and training settings.
Our study shows that: (1) Deep Neural Networks (DNNs) generalize much better on real-world noise.
(2) DNNs may not learn patterns first on real-world noisy data.
(3) When networks are fine-tuned, ImageNet architectures generalize well on noisy data.
(4) Real-world noise appears to be less harmful, yet it is more difficult for robust DNN methods to improve.
(5) Robust learning methods that work well on synthetic noise may not work as well on real-world noise, and vice versa.
We hope our benchmark, as well as our findings, will facilitate deep learning research on noisy data.

assistant: text: The authors establish a large dataset and benchmark of controlled real-world noise for performing controlled experiments on noisy data in deep learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Global Feature Pooling for Fine-grained Visual Categorization

Paper content:
Global feature pooling is a modern variant of feature pooling providing better interpretatability and regularization.
Although alternative pooling methods exist (eg. max, lp norm, stochastic), the averaging operation is still the dominating global pooling scheme in popular models.
As fine-grained recognition requires learning subtle, discriminative features, we consider the question: is average pooling the optimal strategy?
We first ask: ``is there a difference between features learned by global average and max pooling?'' Visualization and quantitative analysis show that max pooling encourages learning features of different spatial scales.
We then ask ``is there a single global feature pooling variant that's most suitable for fine-grained recognition?'' A thorough evaluation of nine representative pooling algorithms finds that: max pooling outperforms average pooling consistently across models, datasets, and image resolutions; it does so by reducing the generalization gap; and generalized pooling's performance increases almost monotonically as it changes from average to max.
We finally ask: ``what's the best way to combine two heterogeneous pooling schemes?'' Common strategies struggle because of potential gradient conflict but the ``freeze-and-train'' trick works best.
We also find that post-global batch normalization helps with faster convergence and improves model performance consistently.

assistant: text: A benchmark of nine representative global pooling schemes reveals some interesting findings.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Monotonic Chunkwise Attention

Paper content:
Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction.
To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed.
We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time.
When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism.
In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.

assistant: text: The paper proposes an extension to a previous monotonic attention model (Raffel et al 2017) to attend to a fixed-sized window up to the alignment position.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep 3D-Zoom Net: Unsupervised Learning of Photo-Realistic 3D-Zoom

Paper content:
The 3D-zoom operation is the positive translation of the camera in the Z-axis, perpendicular to the image plane.
In contrast, the optical zoom changes the focal length and the digital zoom is used to enlarge a certain region of an image to the original image size.
In this paper, we are the first to formulate an unsupervised 3D-zoom learning problem where images with an arbitrary zoom factor can be generated from a given single image.
An unsupervised framework is convenient, as it is a challenging task to obtain a 3D-zoom dataset of natural scenes due to the need for special equipment to ensure camera movement is restricted to the Z-axis.
Besides, the objects in the scenes should not move when being captured, which hinders the construction of a large dataset of outdoor scenes.
We present a novel unsupervised framework to learn how to generate arbitrarily 3D-zoomed versions of a single image, not requiring a 3D-zoom ground truth, called the Deep 3D-Zoom Net.
The Deep 3D-Zoom Net incorporates the following features:
(i) transfer learning from a pre-trained disparity estimation network via a back re-projection reconstruction loss;
(ii) a fully convolutional network architecture that models depth-image-based rendering (DIBR), taking into account high-frequency details without the need for estimating the intermediate disparity; and
(iii) incorporating a discriminator network that acts as a no-reference penalty for unnaturally rendered areas.
Even though there is no baseline to fairly compare our results, our method outperforms previous novel view synthesis research in terms of realistic appearance on large camera baselines.
We performed extensive experiments to verify the effectiveness of our method on the KITTI and Cityscapes datasets.

assistant: text: A method for creating a "zoomed image" for a given input image,and a novel back re-projection reconstruction loss that allows the network to learn underlying 3D structure and maintain a natural appearance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Fixing Weight Decay Regularization in Adam

Paper content:
We note that common implementations of adaptive gradient algorithms, such as Adam, limit the potential benefit of weight decay regularization, because the weights do not decay multiplicatively (as would be expected for standard weight decay) but by an additive constant factor.
We propose a simple way to resolve this issue by decoupling weight decay and the optimization steps taken w.r.t. the loss function.
We provide empirical evidence that our proposed modification
(i) 
decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam, and
(ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter).
We also demonstrate that longer optimization runs require smaller weight decay values for optimal results and introduce a normalized variant of weight decay to reduce this dependence.
Finally, we propose a version of Adam with warm restarts (AdamWR) that has strong anytime performance while achieving state-of-the-art results on CIFAR-10 and ImageNet32x32.
Our source code will become available after the review process.

assistant: text: Investigates weight decay issues lied in the SGD variants and proposes the decoupling method between weight decay and the gradient-based update.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: META LEARNING SHARED HIERARCHIES

Paper content:
We develop a metalearning approach for learning hierarchically structured poli- cies, improving sample efficiency on unseen tasks through the use of shared primitives—policies that are executed for large numbers of timesteps.
Specifi- cally, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies.
We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks.
We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies.
We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes.
We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.

assistant: text: learn hierarchal sub-policies through end-to-end training over a distribution of tasks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Training individually fair ML models with sensitive subspace robustness

Paper content:
We propose an approach to training machine learning models that are fair in the sense that their performance is invariant under certain perturbations to the features.
For example, the performance of a resume screening system should be invariant under changes to the name of the applicant.
We formalize this intuitive notion of fairness by connecting it to the original  notion of individual fairness put forth by Dwork et al and show that the proposed approach achieves this notion of fairness.
We also demonstrate the effectiveness of the approach on two machine learning tasks that are susceptible to gender and racial biases.

assistant: text: This paper proposes a new definition of algorithmic fairness and an algorithm to provably find an ML model that satisfies the fairness contraint.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings

Paper content:
Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter.
Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains.
We introduce XGAN ("Cross-GAN"), a dual adversarial autoencoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions.
We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the model to preserve semantics in the learned embedding space.
We report promising qualitative results for the task of face-to-cartoon translation.
The cartoon dataset we collected for this purpose will also be released as a new benchmark for semantic style transfer.

assistant: text: This paper proposes a new GAN-based model for unpaired image-to-image translation similar to DTN
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Evaluating Robustness of Neural Networks with Mixed Integer Programming

Paper content:
Neural networks trained only to optimize for training accuracy can often be fooled by adversarial examples --- slightly perturbed inputs misclassified with high confidence.
Verification of networks enables us to gauge their vulnerability to such adversarial examples.
We formulate verification of piecewise-linear neural networks as a mixed integer program.
On a representative task of finding minimum adversarial distortions, our verifier is two to three orders of magnitude quicker than the state-of-the-art.
We achieve this computational speedup via tight formulations for non-linearities, as well as a novel presolve algorithm that makes full use of all information available.
The computational speedup allows us to verify properties on convolutional and residual networks with over 100,000 ReLUs --- several orders of magnitude more than networks previously verified by any complete verifier.
In particular, we determine for the first time the exact adversarial accuracy of an MNIST classifier to perturbations with bounded l-∞ norm ε=0.1: for this classifier, we find an adversarial example for 4.38% of samples, and a certificate of robustness to norm-bounded perturbations for the remainder.
Across all robust training procedures and network architectures considered, and for both the MNIST and CIFAR-10 datasets, we are able to certify more samples than the state-of-the-art and find more adversarial examples than a strong first-order attack.

assistant: text: Performs a careful study of mixed integer linear programming approaches for verifying robustness of neural networks to adversarial perturbations and proposes three enhancements to MILP formulations of neural network verification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Decoupling Hierarchical Recurrent Neural Networks With Locally Computable Losses

Paper content:
Learning long-term dependencies is a key long-standing challenge of recurrent neural networks (RNNs).
Hierarchical recurrent neural networks (HRNNs) have been considered a promising approach as long-term dependencies are resolved through shortcuts up and down the hierarchy.
Yet, the memory requirements of Truncated Backpropagation Through Time (TBPTT) still prevent training them on very long sequences.
In this paper, we empirically show that in (deep) HRNNs, propagating gradients back from higher to lower levels can be replaced by locally computable losses, without harming the learning capability of the network, over a wide range of tasks.
This decoupling by local losses reduces the memory requirements of training by a factor exponential in the depth of the hierarchy in comparison to standard TBPTT.

assistant: text: The paper introduces a hierarchical RNN architecture that could be trained more memory efficiently.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage

Paper content:
Models of user behavior are critical inputs in many prescriptive settings and can be viewed as decision rules that transform state information available to the user into actions.
Gaussian processes (GPs), as well as nonlinear extensions thereof, provide a flexible framework to learn user models in conjunction with approximate Bayesian inference.
However, the resulting models may not be interpretable in general.
We propose decision-rule GPs (DRGPs) that apply GPs in a transformed space defined by decision rules that have immediate interpretability to practitioners.
We illustrate this modeling tool on a real application and show that structural variational inference techniques can be used with DRGPs.
We find that DRGPs outperform the direct use of GPs in terms of out-of-sample performance.

assistant: text: We propose a class of user models based on using Gaussian processes applied to a transformed space defined by decision rules
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Incorporating Horizontal Connections in Convolution by Spatial Shuffling

Paper content:
Convolutional Neural Networks (CNNs) are composed of multiple convolution layers and show elegant performance in vision tasks.
The design of the regular convolution is based on the Receptive Field (RF) where the information within a specific region is processed.
In the view of the regular convolution's RF, the outputs of neurons in lower layers with smaller RF are bundled to create neurons in higher layers with larger RF.
As a result, the neurons in high layers are able to capture the global context even though the neurons in low layers only see the local information.
However, in lower layers of the biological brain, the information outside of the RF changes the properties of neurons.
In this work, we extend the regular convolution and propose spatially shuffled convolution (ss convolution).
In ss convolution, the regular convolution is able to use the information outside of its RF by spatial shuffling which is a simple and lightweight operation.
We perform experiments on CIFAR-10 and ImageNet-1k dataset, and show that ss convolution improves the classification performance across various CNNs.

assistant: text: We propose spatially shuffled convolution that the regular convolution incorporates the information from outside of its receptive field.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generating Biased Datasets for Neural Natural Language Processing

Paper content:
In a time where neural networks are increasingly adopted in sensitive applications, algorithmic bias has emerged as an issue with moral implications.
While there are myriad ways that a system may be compromised by bias, systematically isolating and evaluating existing systems on such scenarios is non-trivial, i.e., bias may be subtle, natural and inherently difficult to quantify.
To this end, this paper proposes the first systematic study of benchmarking state-of-the-art neural models against biased scenarios.
More concretely, we postulate that the bias annotator problem can be approximated with neural models, i.e., we propose generative models of latent bias to deliberately and unfairly associate latent features to a specific class.
All in all, our framework provides a new way for principled quantification and evaluation of models against biased datasets.
Consequently, we find that state-of-the-art NLP models (e.g., BERT, RoBERTa, XLNET) are readily compromised by biased data.

assistant: text: We propose a neural bias annotator to benchmark models on their robustness to biased text datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Emergent Communication through Negotiation

Paper content:
Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems.
In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction.
We introduce two communication protocols - one grounded in the semantics of the game, and one which is a priori ungrounded.
We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded, cheap talk channel to do the same.
However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge.
We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.

assistant: text: We teach agents to negotiate using only reinforcement learning; selfish agents can do so, but only using a trustworthy communication channel, and prosocial agents can negotiate using cheap talk.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Paper content:
Training large deep neural networks on massive datasets is  computationally very challenging.
There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue.
The most prominent algorithm in this line of research is LARS, which by  employing layerwise adaptive learning rates trains ResNet on ImageNet in a few minutes.
However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks.
In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches.
Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings.
Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and ResNet-50 training with very little hyperparameter tuning.
In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance.
By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes (Table 1).

assistant: text: This paper develops a layerwise adaptation strategy that allows training BERT models with large 32k mini-batches vs baseline 512.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Write by Learning the Objective

Paper content:
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models for learning prevalent patterns in natural language.
Yet language generated by RNNs often shows several degenerate characteristics that are uncommon in human language; while fluent, RNN language production can be overly generic, repetitive, and even self-contradictory.
We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the abstract qualities of good generation such as Grice’s Maxims.
In this paper, we introduce a general learning framework that can construct a decoding objective better suited for generation.
Starting with a generatively trained RNN language model, our framework learns to construct a substantially stronger generator by combining several discriminatively trained models that can collectively address the limitations of RNN generation.
Human evaluation demonstrates that text generated by the resulting generator is preferred over  that  of  baselines  by  a  large  margin  and  significantly  enhances  the  overall coherence, style, and information content of the generated text.

assistant: text: This paper combines RNN language model with several discriminatively trained models to improve the language generation.