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

Title: Correcting Nuisance Variation using Wasserstein Distance

Paper content:
Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells.
One motivating application is drug development: morphological cell features can be captured from images, from which similarities between different drugs applied at different dosages can be quantified.
The general approach is to find a function mapping the images to an embedding space of manageable dimensionality whose geometry captures relevant features of the input images.
An important known issue for such methods is separating relevant biological signal from nuisance variation.
For example, the embedding vectors tend to be more correlated for cells that were cultured and imaged during the same week than for cells from a different week, despite having identical drug compounds applied in both cases.
In this case, the particular batch a set of experiments were conducted in constitutes the domain of the data; an ideal set of image embeddings should contain only the relevant biological information (e.g. drug effects).
We develop a general framework for adjusting the image embeddings in order to `forget' domain-specific information while preserving relevant biological information.
To do this, we minimize a loss function based on distances between marginal distributions (such as the Wasserstein distance) of embeddings across domains for each replicated treatment.
For the dataset presented, the replicated treatment is the negative control.
We find that for our transformed embeddings (1) the underlying geometric structure is not only preserved but the embeddings also carry improved biological signal (2) less domain-specific information is present.

assistant: text: The authors present a method to remove domain-specific information while preserving the relevant biological information by training a network that minimizes the Wasserstein distance between distrbutions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SIMULTANEOUS ATTRIBUTED NETWORK EMBEDDING AND CLUSTERING

Paper content:
To deal simultaneously with both, the attributed network embedding and clustering, we propose a new model.
It exploits both content and structure information, capitalising on their simultaneous use.
The proposed model relies on the approximation of the relaxed continuous embedding solution by the true discrete clustering one.
Thereby, we show that incorporating an embedding representation provides simpler and more interpretable solutions.
Experiment results demonstrate that the proposed algorithm performs better, in terms of clustering and embedding, than the state-of-art algorithms, including deep learning methods devoted to similar tasks for attributed network datasets with different proprieties.

assistant: text: This paper proposes an algorithm to perform jointly attribute network embedding and clustering together.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks

Paper content:
Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence.
The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices.
This imposes an upper-bound on the reduction of complexity of multiply-accumulate units.
We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training.
Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation.
We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet.
In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline.
We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds.
Overall this analysis enables precise tailoring of computation hardware to the application, yielding area- and power-optimal systems.

assistant: text: We present an analytical framework to determine accumulation bit-width requirements in all three deep learning training GEMMs and verify the validity and tightness of our method via benchmarking experiments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Diversity and Depth in Per-Example Routing Models

Paper content:
Routing models, a form of conditional computation where examples are routed through a subset of components in a larger network, have shown promising results in recent works.
Surprisingly, routing models to date have lacked important properties, such as architectural diversity and large numbers of routing decisions.
Both architectural diversity and routing depth can increase the representational power of a routing network.
In this work, we address both of these deficiencies.
We discuss the significance of architectural diversity in routing models, and explain the tradeoffs between capacity and optimization when increasing routing depth.
In our experiments, we find that adding architectural diversity to routing models significantly improves performance, cutting the error rates of a strong baseline by 35% on an Omniglot setup.
However, when scaling up routing depth, we find that modern routing techniques struggle with optimization.
We conclude by discussing both the positive and negative results, and suggest directions for future research.

assistant: text: This work extends routing networks to use diverse architectures across routed modules
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Understanding Knowledge Graph Representation

Paper content:
Many methods have been developed to represent knowledge graph data, which implicitly exploit low-rank latent structure in the data to encode known information and enable unknown facts to be inferred.
To predict whether a relationship holds between entities, their embeddings are typically compared in the latent space following a relation-specific mapping.
Whilst link prediction has steadily improved, the latent structure, and hence why such models capture semantic information, remains unexplained.
We build on recent theoretical interpretation of word embeddings as a basis to consider an explicit structure for representations of relations between entities.
For identifiable relation types, we are able to predict properties and justify the relative performance of leading knowledge graph representation methods, including their often overlooked ability to make independent predictions.

assistant: text: This paper proposes a detailed study on the explainability of link prediction (LP) models by utilizing a recent interpretation of word embeddings to provide a better understanding of LPs' model performance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A unified theory of adaptive stochastic gradient descent as Bayesian filtering

Paper content:
We formulate stochastic gradient descent (SGD) as a novel factorised Bayesian filtering problem, in which each parameter is inferred separately, conditioned on the corresopnding backpropagated gradient.
Inference in this setting naturally gives rise to BRMSprop and BAdam: Bayesian variants of RMSprop and Adam.
Remarkably, the Bayesian approach recovers many features of state-of-the-art adaptive SGD methods, including amongst others root-mean-square normalization, Nesterov acceleration and AdamW.  As such, the Bayesian approach provides one explanation for the empirical effectiveness of state-of-the-art adaptive SGD algorithms.
Empirically comparing BRMSprop and BAdam with naive RMSprop and Adam on MNIST, we find that Bayesian methods have the potential to considerably reduce test loss and classification error.

assistant: text: The authors attempt to unify existing adaptive gradient methods under the Bayesian filtering framework with the dynamical prior
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Graph Neural Networks with Noisy Labels

Paper content:
We study the robustness to symmetric label noise of GNNs training procedures.
By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present a noise-tolerant approach for the graph classification task.
Our experiments show that test accuracy can be improved under the artificial symmetric noisy setting.

assistant: text: This paper introduces loss correction for Graph Neural Networks to deal with symmetric graph label noise, focused on a graph classification task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Skip Connections Eliminate Singularities

Paper content:
Skip connections made the training of very deep networks possible and have become an indispensable component in a variety of neural architectures.
A completely satisfactory explanation for their success remains elusive.
Here, we present a novel explanation for the benefits of skip connections in training very deep networks.
The difficulty of training deep networks is partly due to the singularities caused by the non-identifiability of the model.
Several such singularities have been identified in previous works:
(i) overlap singularities caused by the permutation symmetry of nodes in a given layer,
(ii) elimination singularities corresponding to the elimination, i.e. consistent deactivation, of nodes,
(iii) singularities generated by the linear dependence of the nodes.
These singularities cause degenerate manifolds in the loss landscape that slow down learning.
We argue that skip connections eliminate these singularities by breaking the permutation symmetry of nodes, by reducing the possibility of node elimination and by making the nodes less linearly dependent.
Moreover, for typical initializations, skip connections move the network away from the "ghosts" of these singularities and sculpt the landscape around them to alleviate the learning slow-down.
These hypotheses are supported by evidence from simplified models, as well as from experiments with deep networks trained on real-world datasets.

assistant: text: The authors show that elimination singularities and overlap singularities impede learning in deep neural networks, and demonstrate that skip connections can reduce the prevalence of these singularities, speeding up learning.
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 the use of optimistic mirror descent to train WGANs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Correcting Nuisance Variation using Wasserstein Distance

Paper content:
Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells.
One motivating application is drug development: morphological cell features can be captured from images, from which similarities between different drugs applied at different dosages can be quantified.
The general approach is to find a function mapping the images to an embedding space of manageable dimensionality whose geometry captures relevant features of the input images.
An important known issue for such methods is separating relevant biological signal from nuisance variation.
For example, the embedding vectors tend to be more correlated for cells that were cultured and imaged during the same week than for cells from a different week, despite having identical drug compounds applied in both cases.
In this case, the particular batch a set of experiments were conducted in constitutes the domain of the data; an ideal set of image embeddings should contain only the relevant biological information (e.g. drug effects).
We develop a general framework for adjusting the image embeddings in order to `forget' domain-specific information while preserving relevant biological information.
To do this, we minimize a loss function based on distances between marginal distributions (such as the Wasserstein distance) of embeddings across domains for each replicated treatment.
For the dataset presented, the replicated treatment is the negative control.
We find that for our transformed embeddings (1) the underlying geometric structure is not only preserved but the embeddings also carry improved biological signal (2) less domain-specific information is present.

assistant: text: We correct nuisance variation for image embeddings across different domains, preserving only relevant information.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Disentanglement using Mixture Latent Space with Continuous and Discrete Variables

Paper content:
Recent advances in deep learning techniques has shown the usefulness of the deep neural networks in extracting features required to perform the task at hand.
However, these features learnt are in particular helpful only for the initial task.
This is due to the fact that the features learnt are very task specific and does not capture the most general and task agnostic features of the input.
In fact the way humans are seen to learn is by disentangling features which task agnostic.
This indicates that leaning task agnostic features by disentangling only the most informative features from the input data.
Recently Variational Auto-Encoders (VAEs) have shown to be the de-facto models to capture the latent variables in a generative sense.
As these latent features can be represented as continuous and/or discrete variables, this indicates us to use VAE with a mixture of continuous and discrete variables for the latent space.
We achieve this by performing our experiments using a modified version of joint-vae to learn the disentangled features.
Feature learning is one of the most fundamental task in machine learning and recently deep learning has made revolutionary advanced in this.
What ever the machine learning task at hand, deep neural networks are excellent models for feature extraction from a raw data.
But the features extracted or learned are very task specific as one use particular loss functions that are suited for task at hand.
For example, cross entropy loss used for multiclass classification problems.This way of learning performs well only for the particular trained task leading to what is called as a narrow or weak artificial intelligence.
However, to achieve the ultimate goal of true or general artificial intelligence, one needs to learn representations in a task agnostic manner.
These task agnostic features should be enough to capture all the required information of the given entity.One such effort made in recent times is towards learning disentangled representations.
As BID0 defines, disentangled representations are the representations where a change in a single unit of the representation corresponds to a change in a single factor of the BID2 .In
this work, we experiment with JointVAE BID2 to explore the disentangled representation for the given dataset Gondal (2019). In
the next sections, we discuss our experimental setup and results.

assistant: text: Mixture Model for Neural Disentanglement
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: This paper proposes a small modification to the monotonic attention in [1] by adding a soft attention to the segment predicted by the monotonic attention.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variance Reduction for Reinforcement Learning in Input-Driven Environments

Paper content:
We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system.
Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking.
Since the state dynamics and rewards depend on the input process, the state alone provides limited information for the expected future returns.
Therefore, policy gradient methods with standard state-dependent baselines suffer high variance during training.
We derive a bias-free, input-dependent baseline to reduce this variance, and analytically show its benefits over state-dependent baselines.
We then propose a meta-learning approach to overcome the complexity of learning a baseline that depends on a long sequence of inputs.
Our experimental results show that across environments from queuing systems, computer networks, and MuJoCo robotic locomotion, input-dependent baselines consistently improve training stability and result in better eventual policies.

assistant: text: For environments dictated partially by external input processes, we derive an input-dependent baseline that provably reduces the variance for policy gradient methods and improves the policy performance in a wide range of RL tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: signSGD with Majority Vote is Communication Efficient and Fault Tolerant

Paper content:
Training neural networks on large datasets can be accelerated by distributing the workload over a network of machines.
As datasets grow ever larger, networks of hundreds or thousands of machines become economically viable.
The time cost of communicating gradients limits the effectiveness of using such large machine counts, as may the increased chance of network faults.
We explore a particularly simple algorithm for robust, communication-efficient learning---signSGD.
Workers transmit only the sign of their gradient vector to a server, and the overall update is decided by a majority vote.
This algorithm uses 32x less communication per iteration than full-precision, distributed SGD.
Under natural conditions verified by experiment, we prove that signSGD converges in the large and mini-batch settings, establishing convergence for a parameter regime of Adam as a byproduct.
Aggregating sign gradients by majority vote means that no individual worker has too much power.
We prove that unlike SGD, majority vote is robust when up to 50% of workers behave adversarially.
The class of adversaries we consider includes as special cases those that invert or randomise their gradient estimate.
On the practical side, we built our distributed training system in Pytorch.
Benchmarking against the state of the art collective communications library (NCCL), our framework---with the parameter server housed entirely on one machine---led to a 25% reduction in time for training resnet50 on Imagenet when using 15 AWS p3.2xlarge machines.

assistant: text: Workers send gradient signs to the server, and the update is decided by majority vote. We show that this algorithm is convergent, communication efficient and fault tolerant, both in theory and in practice.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Gradientless Descent: High-Dimensional Zeroth-Order Optimization

Paper content:
Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$.
In this paper, we present two simple yet powerful GradientLess Descent (GLD) algorithms that do not rely on an underlying gradient estimate and are numerically stable.
We analyze our algorithm from a novel geometric perspective and we show that for {\it any monotone transform} of a smooth and strongly convex objective with latent dimension $k \ge n$, we present a novel analysis that shows convergence within an $\epsilon$-ball of the optimum in $O(kQ\log(n)\log(R/\epsilon))$ evaluations, where the input dimension is $n$, $R$ is the diameter of the input space and $Q$ is the condition number.
Our rates are the first of its kind to be both
1) poly-logarithmically dependent on dimensionality and
2) invariant under monotone transformations.
We further leverage our geometric perspective to show that our analysis is optimal.
Both monotone invariance and its ability to utilize a low latent dimensionality are key to the empirical success of our algorithms, as demonstrated on synthetic and MuJoCo benchmarks.

assistant: text: This paper proposes stable GradientLess Descent (GLD) algorithms that do not rely on gradient estimate.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation

Paper content:
This paper presents a new Graph Neural Network (GNN) type using feature-wise linear modulation (FiLM).
Many standard GNN variants propagate information along the edges of a graph by computing ``messages'' based only on the representation of the source of each edge.
In GNN-FiLM, the representation of the target node of an edge is additionally used to compute a transformation that can be applied to all incoming messages, allowing feature-wise modulation of the passed information.
Results of experiments comparing different GNN architectures on three tasks from the literature are presented, based on re-implementations of baseline methods.
Hyperparameters for all methods were found using extensive search, yielding somewhat surprising results: differences between baseline models are smaller than reported in the literature.
Nonetheless, GNN-FiLM outperforms baseline methods on a regression task on molecular graphs and performs competitively on other tasks.

assistant: text: The paper proposes a new Graph Neural Network architecture that uses Feature-wise Linear Modulation to condition the source-to-target node message-passing based on the target node representation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Overcoming Catastrophic Forgetting via Hessian-free Curvature Estimates

Paper content:
Learning neural networks with gradient descent over a long sequence of tasks is problematic as their fine-tuning to new tasks overwrites the network weights that are important for previous tasks.
This leads to a poor performance on old tasks – a phenomenon framed as catastrophic forgetting.
While early approaches use task rehearsal and growing networks that both limit the scalability of the task sequence orthogonal approaches build on regularization.
Based on the Fisher information matrix (FIM) changes to parameters that are relevant to old tasks are penalized, which forces the task to be mapped into the available remaining capacity of the network.
This requires to calculate the Hessian around a mode, which makes learning tractable.
In this paper, we introduce Hessian-free curvature estimates as an alternative method to actually calculating the Hessian.
In contrast to previous work, we exploit the fact that most regions in the loss surface are flat and hence only calculate a Hessian-vector-product around the surface that is relevant for the current task.
Our experiments show that on a variety of well-known task sequences we either significantly outperform or are en par with previous work.

assistant: text: This paper provides an approach to address catastrophic forgetting via Hessian-free curvature estimates
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Gaggle: Visual Analytics for Model Space Navigation

Paper content:
Recent visual analytics systems make use of multiple machine learning models to better fit the data as opposed to traditional single, pre-defined model systems.
However, while multi-model visual analytic systems can be effective, their added complexity poses usability concerns, as users are required to interact with the parameters of multiple models.
Further, the advent of various model algorithms and associated hyperparameters creates an exhaustive model space to sample models from.
This poses complexity to navigate this model space to find the right model for the data and the task.
In this paper, we present Gaggle, a multi-model visual analytic system that enables users to interactively navigate the model space.
Further translating user interactions into inferences, Gaggle simplifies working with multiple models by automatically finding the best model from the high-dimensional model space to support various user tasks.
Through a qualitative user study, we show how our approach helps users to find a best model for a classification and ranking task.
The study results confirm that Gaggle is intuitive and easy to use, supporting interactive model space navigation and automated model selection without requiring any technical expertise from users.

assistant: text: Gaggle, an interactive visual analytic system to help users interactively navigate model space for classification and ranking tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Paper content:
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.
However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance.
We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively.
Based on these results, we articulate the "lottery ticket hypothesis:" dense, randomly-initialized, feed-forward networks contain subnetworks ("winning tickets") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations.
The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective.
We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations.
We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10.
Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.

assistant: text: Shows that there exists sparse subnetworks that can be trained from scratch with good generalization performance and proposes a unpruned, randomly initialized NNs contain subnetworks that can be trained from scratch with similar generalization accuracy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs

Paper content:
Though state-of-the-art sentence representation models can perform tasks requiring significant knowledge of grammar, it is an open question how best to evaluate their grammatical knowledge.
We explore five experimental methods inspired by prior work evaluating pretrained sentence representation models.
We use a single linguistic phenomenon, negative polarity item (NPI) licensing, as a case study for our experiments.
NPIs like 'any' are grammatical only if they appear in a licensing environment like negation ('Sue doesn't have any cats' vs. '*Sue has any cats').
This phenomenon is challenging because of the variety of NPI licensing environments that exist.
We introduce an artificially generated dataset that manipulates key features of NPI licensing for the experiments.
We find that BERT has significant knowledge of these features, but its success varies widely across different experimental methods.
We conclude that a variety of methods is necessary to reveal all relevant aspects of a model's grammatical knowledge in a given domain.

assistant: text: Different methods for analyzing BERT suggest different (but compatible) conclusions in a case study on NPIs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: RL-ST: Reinforcing Style, Fluency and Content Preservation for Unsupervised Text Style Transfer

Paper content:
Unsupervised text style transfer is the task of re-writing text of a given style into a target style without using a parallel corpus of source style and target style sentences for training.
Style transfer systems are evaluated on their ability to generate sentences that
1) possess the target style,
2) are fluent and natural sounding, and
3) preserve the non-stylistic parts (content) of the source sentence.
We train a reinforcement learning (RL) based unsupervised style transfer system that incorporates rewards for the above measures, and describe novel rewards shaping methods for the same.
Our approach does not attempt to disentangle style and content, and leverages the power of massively pre-trained language models as well as the Transformer.
Our system significantly outperforms existing state-of-art systems based on human as well as automatic evaluations on target style, fluency and content preservation as well as on overall success of style transfer, on a variety of datasets.

assistant: text: A reinforcement learning approach to text style transfer
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: BayesOpt Adversarial Attack

Paper content:
Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input.
Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries.
Therefore, they are not suitable for real-world systems where the maximum query number is limited due to cost.
We propose a query-efficient black-box attack which uses Bayesian optimisation in combination with Bayesian model selection to optimise over the adversarial perturbation and the optimal degree of search space dimension reduction.
We demonstrate empirically that our method can achieve comparable success rates with 2-5 times fewer queries compared to previous state-of-the-art black-box attacks.

assistant: text: The authors propose to use Bayesian optimization with a GP surrogate for adversarial image generation, by exploiting additive structure and using Bayesian model selection to determine an optimal dimensionality reduction.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Regularization Matters in Policy Optimization

Paper content:
Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention  thanks to its encouraging performance on a variety of control tasks.
Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment.
In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.
Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvement on the task performance, and the improvement is typically more significant when the task is more difficult.
We also compare with the widely used entropy regularization and find $L_2$ regularization is generally better.
Our findings are further confirmed to be robust against the choice of training hyperparameters.
We also study the effects of regularizing different components and find that only regularizing the policy network is typically enough.
We hope our study provides guidance for future practices in regularizing policy optimization algorithms.

assistant: text: We show that conventional regularization methods (e.g., $L_2$, dropout), which have been largely ignored in RL methods, can be very effective in policy optimization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: An implicit function learning approach for parametric modal regression

Paper content:
For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean.
Modal regression approaches aim to instead find the conditional mode, but are restricted to nonparametric approaches.
Such approaches can be difficult to scale, and make it difficult to benefit from parametric function approximation, like neural networks, which can learn complex relationships between inputs and targets.
In this work, we propose a parametric modal regression algorithm, by using the implicit function theorem to develop an objective for learning a joint parameterized function over inputs and targets.
We empirically demonstrate on several synthetic problems that our method
(i) can learn multi-valued functions and produce the conditional modes,
(ii) scales well to high-dimensional inputs and
(iii) is even more effective for certain unimodal problems, particularly for high frequency data where the joint function over inputs and targets can better capture the complex relationship between them.
We conclude by showing that our method provides small improvements on two regression datasets that have asymmetric distributions over the targets.

assistant: text: The paper proposes an implicit function approach to learning the modes of multimodal regression.
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: This paper proposes a method to meta-learn a gradient correction module in which preconditioning is parameterized by a neural network, and builds in a two-stage gradient update process during adaptation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Transfer Learning to Learn with Multitask Neural Model Search

Paper content:
Deep learning models require extensive architecture design exploration and hyperparameter optimization to perform well on a given task.
The exploration of the model design space is often made by a human expert, and optimized using a combination of grid search and search heuristics over a large space of possible choices.
Neural Architecture Search (NAS) is a Reinforcement Learning approach that has been proposed to automate architecture design.
NAS has been successfully applied to generate Neural Networks that rival the best human-designed architectures.
However, NAS requires sampling, constructing, and training hundreds to thousands of models to achieve well-performing architectures.
This procedure needs to be executed from scratch for each new task.
The application of NAS to a wide set of tasks currently lacks a way to transfer generalizable knowledge across tasks.
In this paper, we present the Multitask Neural Model Search (MNMS) controller.
Our goal is to learn a generalizable framework that can condition model construction on successful model searches for previously seen tasks, thus significantly speeding up the search for new tasks.
We demonstrate that MNMS can conduct an automated architecture search for multiple tasks simultaneously while still learning well-performing, specialized models for each task.
We then show that pre-trained MNMS controllers can transfer learning to new tasks.
By leveraging knowledge from previous searches, we find that pre-trained MNMS models start from a better location in the search space and reduce search time on unseen tasks, while still discovering models that outperform published human-designed models.

assistant: text: We present Multitask Neural Model Search, a Meta-learner that can design models for multiple tasks simultaneously and transfer learning to unseen tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Transfer Learning to Learn with Multitask Neural Model Search

Paper content:
Deep learning models require extensive architecture design exploration and hyperparameter optimization to perform well on a given task.
The exploration of the model design space is often made by a human expert, and optimized using a combination of grid search and search heuristics over a large space of possible choices.
Neural Architecture Search (NAS) is a Reinforcement Learning approach that has been proposed to automate architecture design.
NAS has been successfully applied to generate Neural Networks that rival the best human-designed architectures.
However, NAS requires sampling, constructing, and training hundreds to thousands of models to achieve well-performing architectures.
This procedure needs to be executed from scratch for each new task.
The application of NAS to a wide set of tasks currently lacks a way to transfer generalizable knowledge across tasks.
In this paper, we present the Multitask Neural Model Search (MNMS) controller.
Our goal is to learn a generalizable framework that can condition model construction on successful model searches for previously seen tasks, thus significantly speeding up the search for new tasks.
We demonstrate that MNMS can conduct an automated architecture search for multiple tasks simultaneously while still learning well-performing, specialized models for each task.
We then show that pre-trained MNMS controllers can transfer learning to new tasks.
By leveraging knowledge from previous searches, we find that pre-trained MNMS models start from a better location in the search space and reduce search time on unseen tasks, while still discovering models that outperform published human-designed models.

assistant: text: In this paper, authors summarize their work on building a framework, called Multitask Neural Model Search controller, for automated neural network construction across multiple tasks simultaneously.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Break-Even Point on the Optimization Trajectories of Deep Neural Networks

Paper content:
Understanding the optimization trajectory is critical to understand training of deep neural networks.
We show how the hyperparameters of stochastic gradient descent influence the covariance of the gradients (K) and the Hessian of the training loss (H) along this trajectory.
Based on a theoretical model, we predict that using a high learning rate or a small batch size in the early phase of training leads SGD to regions of the parameter space with (1) reduced spectral norm of K, and (2) improved conditioning of K and H. We show that the point on the trajectory after which these effects hold, which we refer to as the break-even point, is reached early during training.
We demonstrate these effects empirically for a range of deep neural networks applied to multiple different tasks.
Finally, we apply our analysis to networks with batch normalization (BN) layers and find that it is necessary to use a high learning rate to achieve loss smoothing effects attributed previously to BN alone.

assistant: text: This work analyzes the optimization of deep neural networks by considering how the batch size and step-size hyper-parameters modify learning trajectories.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Empowering Graph Representation Learning with Paired Training and Graph Co-Attention

Paper content:
Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions.
The setup of prediction tasks over entire graphs (such as property prediction for a molecule, or side-effect prediction for a drug), however, proves to be more challenging, as the algorithm must combine evidence about several structurally relevant patches of the graph into a single prediction.
Most prior work attempts to predict these graph-level properties while considering only one graph at a time---not allowing the learner to directly leverage structural similarities and motifs across graphs.
Here we propose a setup in which a graph neural network receives pairs of graphs at once, and extend it with a co-attentional layer that allows node representations to easily exchange structural information across them.
We first show that such a setup provides natural benefits on a pairwise graph classification task (drug-drug interaction prediction), and then expand to a more generic graph regression setup: enhancing predictions over QM9, a standard molecular prediction benchmark.
Our setup is flexible, powerful and makes no assumptions about the underlying dataset properties, beyond anticipating the existence of multiple training graphs.

assistant: text: A method to extend graph-based learning with a co-attentional layer, which outperforms other previous ones on a pairwise graph classification task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tracking Loss: Converting Object Detector to Robust Visual Tracker

Paper content:
In this paper, we find that by designing a novel loss function entitled, ''tracking loss'', Convolutional Neural Network (CNN) based object detectors can be successfully converted to well-performed visual trackers without any extra computational cost.
This property is preferable to visual tracking where annotated video sequences for training are always absent, because rich features learned by detectors from still images could be utilized by dynamic trackers.
It also avoids extra machinery such as feature engineering and feature aggregation proposed in previous studies.
Tracking loss achieves this property by exploiting the internal structure of feature maps within the detection network and treating different feature points discriminatively.
Such structure allows us to simultaneously consider discrimination quality and bounding box accuracy which is found to be crucial to the success.
We also propose a network compression method to accelerate tracking speed without performance reduction.
That also verifies tracking loss will remain highly effective even if the network is drastically compressed.
Furthermore, if we employ a carefully designed tracking loss ensemble, the tracker would be much more robust and accurate.
Evaluation results show that our trackers (including the ensemble tracker and two baseline trackers), outperform all state-of-the-art methods on VOT 2016 Challenge in terms of Expected Average Overlap (EAO) and robustness.
We will make the code publicly available.

assistant: text: We successfully convert a popular detector RPN to a well-performed tracker from the viewpoint of loss function.