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

Title: Extractor-Attention Network: A New Attention Network with Hybrid Encoders for Chinese Text Classification

Paper content:
Chinese text classification has received more and more attention today.
However, the problem of Chinese text representation still hinders the improvement of Chinese text classification, especially the polyphone and the homophone in social media.
To cope with it effectively, we propose a new structure, the Extractor, based on attention mechanisms and design novel attention networks named Extractor-attention network (EAN).
Unlike most of previous works, EAN uses a combination of a word encoder and a Pinyin character encoder instead of a single encoder.
It improves the capability of Chinese text representation.
Moreover, compared with the hybrid encoder methods, EAN has more complex combination architecture and more reducing parameters structures.
Thus, EAN can take advantage of a large amount of information that comes from multi-inputs and alleviates efficiency issues.
The proposed model achieves the state of the art results on 5 large datasets for Chinese text classification.

assistant: text: We propose a novel attention networks with the hybird encoder to solve the text representation issue of Chinese text classification, especially the language phenomena about pronunciations such as the polyphone and the homophone.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Deep Mean Field Games for Modeling Large Population Behavior

Paper content:
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space.
A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions.
We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP.
This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning.
Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population.

assistant: text: Inference of a mean field game (MFG) model of large population behavior via a synthesis of MFG and Markov decision processes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Branched Multi-Task Networks: Deciding What Layers To Share

Paper content:
In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand.
Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers.
Understandably, as the number of possible network configurations is combinatorially large, deciding what layers to share and where to branch out becomes cumbersome.
Prior works have either relied on ad hoc methods to determine the level of layer sharing, which is suboptimal, or utilized neural architecture search techniques to establish the network design, which is considerably expensive.
In this paper, we go beyond these limitations and propose a principled approach to automatically construct branched multi-task networks, by leveraging the employed tasks' affinities.
Given a specific budget, i.e. number of learnable parameters, the proposed approach generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific.
Extensive experimental analysis across numerous, diverse multi-tasking datasets shows that, for a given budget, our method consistently yields networks with the highest performance, while for a certain performance threshold it requires the least amount of learnable parameters.

assistant: text: This paper proposes a novel soft parameter sharing Multi-task Learning framework based on a tree-like structure.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NeuroFabric: Identifying Ideal Topologies for Training A Priori Sparse Networks

Paper content:
Long training times of deep neural networks are a bottleneck in machine learning research.
The major impediment to fast training is the quadratic growth of both memory and compute requirements of dense and convolutional layers with respect to their information bandwidth.
Recently, training `a priori' sparse networks has been proposed as a method for allowing layers to retain high information bandwidth, while keeping memory and compute low.
However, the choice of which sparse topology should be used in these networks is unclear.
In this work, we provide a theoretical foundation for the choice of intra-layer topology.
First, we derive a new sparse neural network initialization scheme that allows us to explore the space of very deep sparse networks.
Next, we evaluate several topologies and show that seemingly similar topologies can often have a large difference in attainable accuracy.
To explain these differences, we develop a data-free heuristic that can evaluate a topology independently from the dataset the network will be trained on.
We then derive a set of requirements that make a good topology, and arrive at a single topology that satisfies all of them.

assistant: text: The paper proposes a sparse cascade architecture that is a multiplication of several sparse matrices and a specific connectivity pattern that outperforms other provided considerations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Self-Supervised GAN Compression

Paper content:
Deep learning's success has led to larger and larger models to handle more and more complex tasks; trained models can contain millions of parameters.
These large models are compute- and memory-intensive, which makes it a challenge to deploy them with minimized latency, throughput, and storage requirements.
Some model compression methods have been successfully applied on image classification and detection or language models, but there has been very little work compressing generative adversarial networks (GANs) performing complex tasks.
In this paper, we show that a standard model compression technique, weight pruning, cannot be applied to GANs using existing methods.
We then develop a self-supervised compression technique which uses the trained discriminator to supervise the training of a compressed generator.
We show that this framework has a compelling performance to high degrees of sparsity, generalizes well to new tasks and models, and enables meaningful comparisons between different pruning granularities.

assistant: text: The authors propose a modification to the classic distillation method for the task of compressing a network to address the failure of previous solutions when applied to generative adversarial networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Scalable Imitation Learning for Multi-Agent Systems with Graph Neural Networks

Paper content:
We propose an implementation of GNN that predicts and imitates the motion be- haviors from observed swarm trajectory data.
The network’s ability to capture interaction dynamics in swarms is demonstrated through transfer learning.
We finally discuss the inherent availability and challenges in the scalability of GNN, and proposed a method to improve it with layer-wise tuning and mixing of data enabled by padding.

assistant: text: This work considers sequence prediction problems in a multi-agent system.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

Paper content:
Learning when to communicate and doing that effectively is essential in multi-agent tasks.
Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks.
In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings.
IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues.
Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases.
Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.

assistant: text: Author proposes a new architecture for multi-agent reinforcement learning that uses several LSTM controllers with tied weights that transmit a continuous vector to each other
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions

Paper content:
The driving force behind deep networks is their ability to compactly represent rich classes of functions.
The primary notion for formally reasoning about this phenomenon is expressive efficiency, which refers to a situation where one network must grow unfeasibly large in order to replicate functions of another.
To date, expressive efficiency analyses focused on the architectural feature of depth, showing that deep networks are representationally superior to shallow ones.
In this paper we study the expressive efficiency brought forth by connectivity, motivated by the observation that modern networks interconnect their layers in elaborate ways.
We focus on dilated convolutional networks, a family of deep models delivering state of the art performance in sequence processing tasks.
By introducing and analyzing the concept of mixed tensor decompositions, we prove that interconnecting dilated convolutional networks can lead to expressive efficiency.
In particular, we show that even a single connection between intermediate layers can already lead to an almost quadratic gap, which in large-scale settings typically makes the difference between a model that is practical and one that is not.
Empirical evaluation demonstrates how the expressive efficiency of connectivity, similarly to that of depth, translates into gains in accuracy.
This leads us to believe that expressive efficiency may serve a key role in developing new tools for deep network design.

assistant: text: Shows that the WaveNet's structural assumption of a single perfect binary tree is hindering its performance and that WaveNet-like architectures with more complex mixed tree structures perform better.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MMA Training: Direct Input Space Margin Maximization through Adversarial Training

Paper content:
We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary.
Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the "shortest successful perturbation", demonstrating a close connection between adversarial losses and the margins.
We propose Max-Margin Adversarial (MMA) training to directly maximize the margins to achieve adversarial robustness.
Instead of adversarial training with a fixed $\epsilon$, MMA offers an improvement by enabling adaptive selection of the "correct" $\epsilon$ as the margin individually for each datapoint.
In addition, we rigorously analyze adversarial training with the perspective of margin maximization, and provide an alternative interpretation for adversarial training, maximizing either a lower or an upper bound of the margins.
Our experiments empirically confirm our theory and demonstrate MMA training's efficacy on the MNIST and CIFAR10 datasets w.r.t. $\ell_\infty$ and $\ell_2$ robustness.

assistant: text: A method for robust learning against adversarial attacks where the input space margin is directly maximized and a softmax variant of the max-margin is introduced.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories

Paper content:
We address the problem of learning to discover 3D parts for objects in unseen categories.
Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose fundamental challenges on data-driven shape segmentation approaches.
Formulated as a contextual bandit problem, we propose a learning-based iterative grouping framework which learns a grouping policy to progressively merge small part proposals into bigger ones in a bottom-up fashion.
At the core of our approach is to restrict the local context for extracting part-level features, which encourages the generalizability to novel categories.
On a recently proposed large-scale fine-grained 3D part dataset, PartNet, we demonstrate that our method can transfer knowledge of parts learned from 3 training categories to 21 unseen testing categories without seeing any annotated samples.
Quantitative comparisons against four strong shape segmentation baselines show that we achieve the state-of-the-art performance.

assistant: text: A method for segmenting 3D point clouds of objects into component parts, focused on generalizing part groupings to novel object categories unseen during training, that shows strong performance relative to baselines.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control

Paper content:
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.
However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entropy regularization to prevent policy collapse.
As an alternative to policy gradient algorithms, we introduce V-MPO, an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) that performs policy iteration based on a learned state-value function.
We show that V-MPO surpasses previously reported scores for both the Atari-57 and DMLab-30 benchmark suites in the multi-task setting, and does so reliably without importance weighting, entropy regularization, or population-based tuning of hyperparameters.
On individual DMLab and Atari levels, the proposed algorithm can achieve scores that are substantially higher than has previously been reported.
V-MPO is also applicable to problems with high-dimensional, continuous action spaces, which we demonstrate in the context of learning to control simulated humanoids with 22 degrees of freedom from full state observations and 56 degrees of freedom from pixel observations, as well as example OpenAI Gym tasks where V-MPO achieves substantially higher asymptotic scores than previously reported.

assistant: text: A state-value function-based version of MPO that achieves good results in a wide range of tasks in discrete and continuous control.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Black Box Recursive Translations for Molecular Optimization

Paper content:
Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery.
We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties.
Remarkably, we observe that when generated molecules are iteratively fed back into the translator, molecular compound attributes improve with each step.
We show that this finding is invariant to the choice of translation model, making this a "black box" algorithm.
We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization.
This simple, powerful technique operates strictly on the inputs and outputs of any translation model.
We obtain new state-of-the-art results for molecular property optimization tasks using our simple drop-in replacement with well-known sequence and graph-based models.
Our method provides a significant boost in performance relative to its non-recursive peers with just a simple "``for" loop.
Further, BBRT is highly interpretable, allowing users to map the evolution of newly discovered compounds from known starting points.

assistant: text: The paper builds on existing translation models developed for molecular optimization, making an iterative use of sequence to sequence or graph to graph translation models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DeFINE: Deep Factorized Input Word Embeddings for Neural Sequence Modeling

Paper content:
For sequence models with large word-level vocabularies, a majority of network parameters lie in the input and output layers.
In this work, we describe a new method, DeFINE, for learning deep word-level representations efficiently.
Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of low dimensional input and output layers, reducing total parameters and training time while delivering similar or better performance versus existing methods.
DeFINE can be incorporated easily in new or existing sequence models.
Compared to state-of-the-art methods including adaptive input representations, this technique results in a 6% to 20% drop in perplexity.
On WikiText-103, DeFINE reduces total parameters of Transformer-XL by half with minimal impact on performance.
On the Penn Treebank, DeFINE improves AWD-LSTM by 4 points with a 17% reduction in parameters,  achieving comparable performance to state-of-the-art methods with fewer parameters.
For machine translation, DeFINE improves a Transformer model by 2% while simultaneously reducing total parameters by 26%

assistant: text: This paper describes a new method for learning deep word-level representations efficiently by using a hierarchical structure with skip-connections for the use of low dimensional input and output layers.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Critical initialisation in continuous approximations of binary neural networks

Paper content:
The training of stochastic neural network models with binary ($\pm1$) weights and activations via continuous surrogate networks is investigated.
We derive, using mean field theory, a set of scalar equations describing how input signals propagate through surrogate networks.
The equations reveal that depending on the choice of surrogate model, the networks may or may not exhibit an order to chaos transition, and the presence of depth scales that limit the maximum trainable depth.
Specifically, in solving the equations for edge of chaos conditions, we show that surrogates derived using the Gaussian local reparameterisation trick have no critical initialisation, whereas a deterministic surrogates based on analytic Gaussian integration do.
The theory is applied to a range of binary neuron and weight design choices, such as different neuron noise models, allowing the categorisation of algorithms in terms of their behaviour at initialisation.
Moreover, we predict theoretically and confirm numerically, that common weight initialization schemes used in standard continuous networks, when applied to the mean values of the stochastic binary weights, yield poor training performance.
This study shows that, contrary to common intuition, the means of the stochastic binary weights should be initialised close to close to $\pm 1$ for deeper networks to be trainable.

assistant: text: The authors investigate the training dynamics of binary neural networks when using continuous surrogates, study what properties networks should have at initialization to best train, and provide concrete advice about stochastic weights at initialization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Universality Patterns in the Training of Neural Networks

Paper content:
This paper proposes and demonstrates a surprising pattern in the training of neural networks: there is a one to one relation between the values of any pair of losses (such as cross entropy, mean squared error, 0/1 error etc.) evaluated for a model arising at (any point of) a training run.
This pattern is universal in the sense that this one to one relationship is identical across architectures (such as VGG, Resnet, Densenet etc.), algorithms (SGD and SGD with momentum) and training loss functions (cross entropy and mean squared error).

assistant: text: We identify some universal patterns (i.e., holding across architectures) in the behavior of different surrogate losses (CE, MSE, 0-1 loss) while training neural networks and present supporting empirical evidence.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Universality, Robustness, and Detectability of Adversarial Perturbations under Adversarial Training

Paper content:
Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space.
While adversarial training improves the robustness of classifiers against such adversarial perturbations, it leaves classifiers sensitive to them on a non-negligible fraction of the inputs.
We argue that there are two different kinds of adversarial perturbations: shared perturbations which fool a classifier on many inputs and singular perturbations which only fool the classifier on a small fraction of the data.
We find that adversarial training increases the robustness of classifiers against shared perturbations.
Moreover, it is particularly effective in removing universal perturbations, which can be seen as an extreme form of shared perturbations.
Unfortunately, adversarial training does not consistently increase the robustness against singular perturbations on unseen inputs.
However, we find that adversarial training decreases robustness of the remaining perturbations against image transformations such as changes to contrast and brightness or  Gaussian blurring.
It thus makes successful attacks on the classifier in the physical world less likely.
Finally, we show that even singular perturbations can be easily detected and must thus exhibit generalizable patterns even though the perturbations are specific for certain inputs.

assistant: text: Analyses adversarial training and its effect on universal adversarial examples as well as standard (basic iteration) adversarial examples and how adversarial training affects detection.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Identifying and Controlling Important Neurons in Neural Machine Translation

Paper content:
Neural machine translation (NMT) models learn representations containing substantial linguistic information.
However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons.
We develop unsupervised methods for discovering important neurons in NMT models.
Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision.
We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena.
Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons.

assistant: text: This work proposes finding "meaningful" neurons in Neural Machine Translation models by ranking based on correlation between pairs of models, different epochs, or different datasets, and proposes a controlling mechanism for the models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Consistent Performance on Atari using Expert Demonstrations

Paper content:
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.
We identify three key challenges that any algorithm needs to master in order to perform well on all games:  processing diverse reward distributions, reasoning over long time horizons, and exploring efficiently.
In this paper, we propose an algorithm that addresses each of these challenges and is able to learn human-level policies on nearly all Atari games.
A new transformed Bellman operator allows our algorithm to process rewards of varying densities and scales; an auxiliary temporal consistency loss allows us to train stably using a discount factor of 0.999 (instead of 0.99) extending the effective planning horizon by an order of magnitude; and we ease the exploration problem by using human demonstrations that guide the agent towards rewarding states.
When tested on a set of 42 Atari games, our algorithm exceeds the performance  of an average human on 40 games using a common set of hyper parameters.

assistant: text: This paper proposes a transformed Bellman operator that aims to solve sensitivity to unclipped reward, robustness to the value of the discount factor, and the exploration problem.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Counting the Paths in Deep Neural Networks as a Performance Predictor

Paper content:
We propose a novel quantitative measure to predict the performance of a deep neural network classifier, where the measure is derived exclusively from the graph structure of the network.
We expect that this measure is a fundamental first step in developing a method to evaluate new network architectures and reduce the reliance on the computationally expensive trial and error or "brute force" optimisation processes involved in model selection.
The measure is derived in the context of multi-layer perceptrons (MLPs), but the definitions are shown to be useful also in the context of deep convolutional neural networks (CNN), where it is able to estimate and compare the relative performance of different types of neural networks, such as VGG, ResNet, and DenseNet.
Our measure is also used to study the effects of some important "hidden" hyper-parameters of the DenseNet architecture, such as number of layers, growth rate and the dimension of 1x1 convolutions in DenseNet-BC.
Ultimately, our measure facilitates the optimisation of the DenseNet design, which shows improved results compared to the baseline.

assistant: text: A quantitative measure to predict the performances of deep neural network models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Actionable Representations with Goal Conditioned Policies

Paper content:
Representation learning is a central challenge across a range of machine learning areas.
In reinforcement learning, effective and functional representations have the potential to tremendously accelerate learning progress and solve more challenging problems.
Most prior work on representation learning has focused on generative approaches, learning representations that capture all the underlying factors of variation in the observation space in a more disentangled or well-ordered manner.
In this paper, we instead aim to learn functionally salient representations: representations that are not necessarily complete in terms of capturing all factors of variation in the observation space, but rather aim to capture those factors of variation that are important for decision making -- that are "actionable".
These representations are aware of the dynamics of the environment, and capture only the elements of the observation that are necessary for decision making rather than all factors of variation, eliminating the need for explicit reconstruction.
We show how these learned representations can be useful to improve exploration for sparse reward problems, to enable long horizon hierarchical reinforcement learning, and as a state representation for learning policies for downstream tasks.
We evaluate our method on a number of simulated environments, and compare it to prior methods for representation learning, exploration, and hierarchical reinforcement learning.

assistant: text: An approach to representation learning in the context of reinforcement learning that distinguishes two stages functionally in terms of the actions that are needed to reach them.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Inferring hierarchies of latent features in calcium imaging data

Paper content:
A key problem in neuroscience and life sciences more generally is that the data generation process is often best thought of as a hierarchy of dynamic systems.
One example of this is in-vivo calcium imaging data, where observed calcium transients are driven by a combination of electro-chemical kinetics where hypothesized trajectories around manifolds determining the frequency of these transients.
A recent approach using sequential variational auto-encoders demonstrated it was possible to learn the latent dynamic structure of reaching behaviour from spiking data modelled as a Poisson process.
Here we extend this approach using a ladder method to infer the spiking events driving calcium transients along with the deeper latent dynamic system.
We show strong performance of this approach on a benchmark synthetic dataset against a number of alternatives.

assistant: text: We propose an extension to LFADS capable of inferring spike trains to reconstruct calcium fluorescence traces using hierarchical VAEs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient Augmentation via Data Subsampling

Paper content:
Data augmentation is commonly used to encode invariances in learning methods.
However, this process is often performed in an inefficient manner, as artificial examples are created by applying a number of transformations to all points in the training set.
The resulting explosion of the dataset size can be an issue in terms of storage and training costs, as well as in selecting and tuning the optimal set of transformations to apply.
In this work, we demonstrate that it is possible to significantly reduce the number of data points included in data augmentation while realizing the same accuracy and invariance benefits of augmenting the entire dataset.
We propose a novel set of subsampling policies, based on model influence and loss, that can achieve a 90% reduction in augmentation set size while maintaining the accuracy gains of standard data augmentation.

assistant: text: Selectively augmenting difficult to classify points results in efficient training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Continuous-fidelity Bayesian Optimization with Knowledge Gradient

Paper content:
While Bayesian optimization (BO) has achieved great success in optimizing expensive-to-evaluate black-box functions, especially tuning hyperparameters of neural networks, methods such as random search (Li et al., 2016) and multi-fidelity BO (e.g. Klein et al. (2017)) that exploit cheap approximations, e.g. training on a smaller training data or with fewer iterations, can outperform standard BO approaches that use only full-fidelity observations.
In this paper, we propose a novel Bayesian optimization algorithm, the continuous-fidelity knowledge gradient (cfKG) method, that can be used when fidelity is controlled by one or more continuous settings such as training data size and the number of training iterations.
cfKG characterizes the value of the information gained by sampling a point at a given fidelity, choosing to sample at the point and fidelity with the largest value per unit cost.
Furthermore, cfKG can be generalized, following Wu et al. (2017), to settings where derivatives are available in the optimization process, e.g. large-scale kernel learning, and where more than one point can be evaluated simultaneously.
Numerical experiments show that cfKG outperforms state-of-art algorithms when optimizing synthetic functions, tuning convolutional neural networks (CNNs) on CIFAR-10 and SVHN, and in large-scale kernel learning.

assistant: text: We propose a Bayes-optimal Bayesian optimization algorithm for hyperparameter tuning by exploiting cheap approximations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Deep Latent-variable MRFs with Amortized Bethe Free Energy Minimization

Paper content:
While much recent work has targeted learning deep discrete latent variable models with variational inference, this setting remains challenging, and it is often necessary to make use of potentially high-variance gradient estimators in optimizing the ELBO.
As an alternative, we propose to optimize a non-ELBO objective derived from the Bethe free energy approximation to an MRF's partition function.
This objective gives rise to a saddle-point learning problem, which we train inference networks to approximately optimize.
The derived objective requires no sampling, and can be efficiently computed for many MRFs of interest.
We evaluate the proposed approach in learning high-order neural HMMs on text, and find that it often outperforms other approximate inference schemes in terms of true held-out log likelihood.
At the same time, we find that all the approximate inference-based approaches to learning high-order neural HMMs we consider underperform learning with exact inference by a significant margin.

assistant: text: Learning deep latent variable MRFs with a saddle-point objective derived from the Bethe partition function approximation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The divergences minimized by non-saturating GAN training

Paper content:
Interpreting generative adversarial network (GAN) training as approximate divergence minimization has been
theoretically insightful, has spurred discussion, and has lead to theoretically and practically interesting
extensions such as f-GANs and Wasserstein GANs.
For both classic GANs and f-GANs, there is an original variant of training and a "non-saturating" variant which uses an alternative form of generator gradient.
The original variant is theoretically easier to study, but for GANs the alternative variant performs better in practice.
The non-saturating scheme is often regarded as a simple modification to deal with optimization issues, but we show that in fact the non-saturating scheme for GANs is effectively optimizing a reverse KL-like f-divergence.
We also develop a number of theoretical tools to help compare and classify f-divergences.
We hope these results may help to clarify some of the theoretical discussion surrounding the divergence minimization view of GAN training.

assistant: text: Non-saturating GAN training effectively minimizes a reverse KL-like f-divergence.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization

Paper content:
Batch Normalization (BN) is one of the most widely used techniques in Deep Learning field.
But its performance can awfully degrade with insufficient batch size.
This weakness limits the usage of BN on many computer vision tasks like detection or segmentation, where batch size is usually small due to the constraint of memory consumption.
Therefore many modified normalization techniques have been proposed, which either fail to restore the performance of BN completely, or have to introduce additional nonlinear operations in inference procedure and increase huge consumption.
In this paper, we reveal that there are two extra batch statistics involved in backward propagation of BN, on which has never been well discussed before.
The extra batch statistics associated with gradients also can severely affect the training of deep neural network.
Based on our analysis, we propose a novel normalization method, named Moving Average Batch Normalization (MABN).
MABN can completely restore the performance of vanilla BN in small batch cases, without introducing any additional nonlinear operations in inference procedure.
We prove the benefits of MABN by both theoretical analysis and experiments.
Our experiments demonstrate the effectiveness of MABN in multiple computer vision tasks including ImageNet and COCO.
The code has been released in https://github.com/megvii-model/MABN.

assistant: text: A method to deal with the small batch size problem of BN which applies moving average operation without too much overhead and reduces the number of statistics of BN for better stability.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Minimizing Change in Classifier Likelihood to Mitigate Catastrophic Forgetting

Paper content:
Continual learning is a longstanding goal of artificial intelligence, but is often counfounded by catastrophic forgetting that prevents neural networks from learning tasks sequentially.
Previous methods in continual learning have demonstrated how to mitigate catastrophic forgetting, and learn new tasks while retaining performance on the previous tasks.
We analyze catastrophic forgetting from the perspective of change in classifier likelihood and propose a simple L1 minimization criterion which can be adapted to different use cases.
We further investigate two ways to minimize forgetting as quantified by this criterion and propose strategies to achieve finer control over forgetting.
Finally, we evaluate our strategies on 3 datasets of varying difficulty and demonstrate improvements over previously known L2 strategies for mitigating catastrophic forgetting.

assistant: text: This paper tries to solve the continual learning prolem by focusing on regularization approaches, and it proposes a L_1 strategy to mitigate the problem.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DeepSphere: a graph-based spherical CNN

Paper content:
Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance.
DeepSphere, a method based on a graph representation of the discretized sphere, strikes a controllable balance between these two desiderata.
This contribution is twofold.
First, we study both theoretically and empirically how equivariance is affected by the underlying graph with respect to the number of pixels and neighbors.
Second, we evaluate DeepSphere on relevant problems.
Experiments show state-of-the-art performance and demonstrates the efficiency and flexibility of this formulation.
Perhaps surprisingly, comparison with previous work suggests that anisotropic filters might be an unnecessary price to pay.

assistant: text: Combines existing CNN frameworks based on the discretization of a sphere as a graph to show a convergence result which is related to the rotation equivalence on a sphere.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: $\textrm{D}^2$GAN: A Few-Shot Learning Approach with Diverse and Discriminative Feature Synthesis

Paper content:
The rich and accessible labeled data fuel the revolutionary success of deep learning.
Nonetheless, massive supervision remains a luxury for many real applications, boosting great interest in label-scarce techniques such as few-shot learning (FSL).
An intuitively feasible approach to FSL is to conduct data augmentation via synthesizing additional training samples.
The key to this approach is how to guarantee both discriminability and diversity of the synthesized samples.
In this paper, we propose a novel FSL model, called $\textrm{D}^2$GAN, which synthesizes Diverse and Discriminative features based on Generative Adversarial Networks (GAN).
$\textrm{D}^2$GAN secures discriminability of the synthesized features by constraining them to have high correlation with real features of the same classes while low correlation with those of different classes.
Based on the observation that noise vectors that are closer in the latent code space are more likely to be collapsed into the same mode when mapped to feature space, $\textrm{D}^2$GAN incorporates a novel anti-collapse regularization term, which encourages feature diversity by penalizing the ratio of the logarithmic similarity of two synthesized features and the logarithmic similarity of the latent codes generating them.
Experiments on three common benchmark datasets verify the effectiveness of $\textrm{D}^2$GAN by comparing with the state-of-the-art.
The rich and accessible labeled data fuel the revolutionary success of deep learning.
Nonetheless, massive supervision remains a luxury for many real applications, boosting great interest in label-scarce techniques such as few-shot learning (FSL).
An intuitively feasible approach to FSL is to conduct data augmentation via synthesizing additional training samples.
The key to this approach is how to guarantee both discriminability and diversity of the synthesized samples.
In this paper, we propose a novel FSL model, called D 2 GAN, which synthesizes Diverse and Discriminative features based on Generative Adversarial Networks (GAN).
D 2 GAN secures discriminability of the synthesized features by constraining them to have high correlation with real features of the same classes while low correlation with those of different classes.
Based on the observation that noise vectors that are closer in the latent code space are more likely to be collapsed into the same mode when mapped to feature space, D 2 GAN incorporates a novel anti-collapse regularization term, which encourages feature diversity by penalizing the ratio of the logarithmic similarity of two synthesized features and the logarithmic similarity of the latent codes generating them.
Experiments on three common benchmark datasets verify the effectiveness of D 2 GAN by comparing with the state-of-the-art.

assistant: text: A meta-learning method that learns a generative model that can augment the support set of a few-shot learner which optimizes a combination of losses.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Enhancing Generalization of First-Order Meta-Learning

Paper content:
In this study we focus on first-order meta-learning algorithms that aim to learn a parameter initialization of a network which can quickly adapt to new concepts, given a few examples.
We investigate two approaches to enhance generalization and speed of learning of such algorithms, particularly expanding on the Reptile (Nichol et al., 2018) algorithm.
We introduce a novel regularization technique called meta-step gradient pruning and also investigate the effects of increasing the depth of network architectures in first-order meta-learning.
We present an empirical evaluation of both approaches, where we match benchmark few-shot image classification results with 10 times fewer iterations using Mini-ImageNet dataset and with the use of deeper networks, we attain accuracies that surpass the current benchmarks of few-shot image classification using Omniglot dataset.

assistant: text: The study introduces two approaches to enhance generalization of first-order meta-learning and presents empirical evaluation on few-shot image classification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ProxSGD: Training Structured Neural Networks under Regularization and Constraints

Paper content:
In this paper, we consider the problem of training neural networks (NN).
To promote a NN with specific structures, we explicitly take into consideration the nonsmooth regularization (such as L1-norm) and constraints (such as interval constraint).
This is formulated as a constrained nonsmooth nonconvex optimization problem, and we propose a convergent proximal-type stochastic gradient descent (Prox-SGD) algorithm.
We show that under properly selected learning rates, momentum eventually resembles the unknown real gradient and thus is crucial in analyzing the convergence.
We establish that with probability 1, every limit point of the sequence generated by the proposed Prox-SGD is a stationary point.
Then the Prox-SGD is tailored to train a sparse neural network and a binary neural network, and the theoretical analysis is also supported by extensive numerical tests.

assistant: text: The paper proposes a new gradient-based stochastic optimization algorithm with gradient averaging by adapting theory for proximal algorithms to the non-convex setting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks

Paper content:
Imitation learning from human-expert demonstrations has been shown to be greatly helpful for challenging reinforcement learning problems with sparse environment rewards.
However, it is very difficult to achieve similar success without relying on expert demonstrations.
Recent works on self-imitation learning showed that imitating the agent's own past good experience could indirectly drive exploration in some environments, but these methods often lead to sub-optimal and myopic behavior.
To address this issue, we argue that exploration in diverse directions by imitating diverse trajectories, instead of focusing on limited good trajectories, is more desirable for the hard-exploration tasks.
We propose a new method of learning a trajectory-conditioned policy to imitate diverse trajectories from the agent's own past experiences and show that such self-imitation helps avoid myopic behavior and increases the chance of finding a globally optimal solution for hard-exploration tasks, especially when there are misleading rewards.
Our method significantly outperforms existing self-imitation learning and count-based exploration methods on various hard-exploration tasks with local optima.
In particular, we report a state-of-the-art score of more than 20,000 points on Montezumas Revenge without using expert demonstrations or resetting to arbitrary states.

assistant: text: This paper addresses hard exploration tasks by applying self-imitation to a diverse selection of trajectories from past experience, to drive more efficient exploration in sparse-reward problems, achieving SOTA results.