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

Title: Structured Pruning for Efficient ConvNets via Incremental Regularization

Paper content:
Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance loss.
Despite its effectiveness, existing regularization-based parameter pruning methods usually drive weights towards zero with large and constant regularization factors, which neglects the fact that the expressiveness of CNNs is fragile and needs a more gentle way of regularization for the networks to adapt during pruning.
To solve this problem, we propose a new regularization-based pruning method (named IncReg) to incrementally assign different regularization factors to different weight groups based on their relative importance, whose effectiveness is proved on popular CNNs compared with state-of-the-art methods.

assistant: text: This paper proposes a regularization-based pruning method to incrementally assign different regularization factors to different weight groups based on their relative importance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep reinforcement learning with relational inductive biases

Paper content:
We introduce an approach for augmenting model-free deep reinforcement learning agents with a mechanism for relational reasoning over structured representations, which improves performance, learning efficiency, generalization, and interpretability.
Our architecture encodes an image as a set of vectors, and applies an iterative message-passing procedure to discover and reason about relevant entities and relations in a scene.
In six of seven StarCraft II Learning Environment mini-games, our agent achieved state-of-the-art performance, and surpassed human grandmaster-level on four.
In a novel navigation and planning task, our agent's performance and learning efficiency far exceeded non-relational baselines, it was able to generalize to more complex scenes than it had experienced during training.
Moreover, when we examined its learned internal representations, they reflected important structure about the problem and the agent's intentions.
The main contribution of this work is to introduce techniques for representing and reasoning about states in model-free deep reinforcement learning agents via relational inductive biases.
Our experiments show this approach can offer advantages in efficiency, generalization, and interpretability, and can scale up to meet some of the most challenging test environments in modern artificial intelligence.

assistant: text: A quantitative and qualitative analysis and evaluation of the self-attention mechanism combined with relation network in the context of model-free RL.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Approximation capability of neural networks on sets of probability measures and tree-structured data

Paper content:
This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures.
By doing so the work parallels a more then a decade old results on mean-map embedding of probability measures in reproducing kernel Hilbert spaces.
The work has wide practical consequences for multi-instance learning, where it theoretically justifies some recently proposed constructions.
The result is then extended to Cartesian products, yielding universal approximation theorem for tree-structured domains, which naturally occur in data-exchange formats like JSON, XML, YAML, AVRO, and ProtoBuffer.
This has important practical implications, as it enables to automatically create an architecture of neural networks for processing structured data (AutoML paradigms), as demonstrated by an accompanied library for JSON format.

assistant: text: This paper investigates the approximation properties of a family of neural networks designed to address multi-instance learning problems, and shows that results for standard one layer architectures extend to these models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Synthetic vs Real: Deep Learning on Controlled Noise

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

assistant: text: We establish a benchmark of controlled real noise and reveal several interesting findings about real-world noisy data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Ranking Policy Gradient

Paper content:
Sample inefficiency is a long-lasting problem in reinforcement learning (RL).
The state-of-the-art uses action value function to derive policy while it usually involves an extensive search over the state-action space and unstable optimization.
Towards the sample-efficient RL, we propose ranking policy gradient (RPG), a policy gradient method that learns the optimal rank of a set of discrete actions.
To accelerate the learning of policy gradient methods, we establish the equivalence between maximizing the lower bound of return and imitating a near-optimal policy without accessing any oracles.
These results lead to a general off-policy learning framework, which preserves the optimality, reduces variance, and improves the sample-efficiency.
We conduct extensive experiments showing that when consolidating with the off-policy learning framework, RPG substantially reduces the sample complexity, comparing to the state-of-the-art.

assistant: text: We propose ranking policy gradient that learns the optimal rank of actions to maximize return. We propose a general off-policy learning framework with the properties of optimality preserving, variance reduction, and sample-efficiency.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Evidential Uncertainty

Paper content:
Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust and efficient measures of uncertainty are crucial.
While it is possible to train regression networks to output the parameters of a probability distribution by maximizing a Gaussian likelihood function, the resulting model remains oblivious to the underlying confidence of its predictions.
In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target.
We accomplish this by  placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution.
We impose priors during training such that the model is penalized when its predicted evidence is not aligned with the correct output.
Thus the model estimates not only the probabilistic mean and variance of our target but also the underlying uncertainty associated with each of those parameters.
We observe that our evidential regression method learns well-calibrated measures of uncertainty on various benchmarks, scales to complex computer vision tasks, and is robust to adversarial input perturbations.

assistant: text: Fast, calibrated uncertainty estimation for neural networks without sampling
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively

Paper content:
The learning of hierarchical representations for image classification has experienced an impressive series of successes due in part to the availability of large-scale labeled data for training.
On the other hand, the trained classifiers have traditionally been evaluated on a handful of test images, which are deemed to be extremely sparsely distributed in the space of all natural images.
It is thus questionable whether recent performance improvements on the excessively re-used test sets generalize to real-world natural images with much richer content variations.
In addition, studies on adversarial learning show that it is effortless to construct adversarial examples that fool nearly all image classifiers, adding more complications to relative performance comparison of existing models.
This work presents an efficient framework for comparing image classifiers, which we name the MAximum Discrepancy (MAD) competition.
Rather than comparing image classifiers on fixed test sets, we adaptively sample a test set from an arbitrarily large corpus of unlabeled images so as to maximize the discrepancies between the classifiers, measured by the distance over WordNet hierarchy.
Human labeling on the resulting small and model-dependent image sets reveals the relative performance of the competing classifiers and provides useful insights on potential ways to improve them.
We report the MAD competition results of eleven ImageNet classifiers while noting that the framework is readily extensible and cost-effective to add future classifiers into the competition.

assistant: text: Error spotting mechanism which compares image classifiers by sampling their "most disagreed" test set, measuring disagreement through a semantics-aware distance derived form WordNet ontology.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Distributed Fine-tuning of Language Models on Private Data

Paper content:
One of the big challenges in machine learning applications is that training data can be different from the real-world data faced by the algorithm.
In language modeling, users’ language (e.g. in private messaging) could change in a year and be completely different from what we observe in publicly available data.
At the same time, public data can be used for obtaining general knowledge (i.e. general model of English).
We study approaches to distributed fine-tuning of a general model on user private data with the additional requirements of maintaining the quality on the general data and minimization of communication costs.
We propose a novel technique that significantly improves prediction quality on users’ language compared to a general model and outperforms gradient compression methods in terms of communication efficiency.
The proposed procedure is fast and leads to an almost 70% perplexity reduction and 8.7 percentage point improvement in keystroke saving rate on informal English texts.
Finally, we propose an experimental framework for evaluating differential privacy of distributed training of language models and show that our approach has good privacy guarantees.

assistant: text: This paper deals with improving language models on mobile equipments based on small portion of text that the user has inputted by employing a linearly interpolated objectives between user specific text and general English.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Global Convergence  of Training Deep Linear ResNets

Paper content:
We study the convergence of gradient descent (GD) and stochastic gradient descent (SGD) for training $L$-hidden-layer linear residual networks (ResNets).
We prove that for training deep residual networks with certain linear transformations at input and output layers, which are fixed throughout training, both GD and SGD with zero initialization on all hidden weights can converge to the global minimum of the training loss.
Moreover, when specializing to appropriate Gaussian random linear transformations, GD and SGD provably optimize wide enough deep linear ResNets.
Compared with the global convergence result of GD for training standard deep linear networks \citep{du2019width}, our condition on the neural network width is sharper by a factor of $O(\kappa L)$, where $\kappa$ denotes the condition number of the covariance matrix of the training data.
In addition, for the first time we establish the global convergence of SGD for training deep linear ResNets and prove a linear convergence rate when the global minimum is $0$.

assistant: text: Study of convergence properties of GD and SGD on deep linear resnets, and proof that under certain conditions on the input and output transformations and with zero initialization, GD and SGD converges to global minima.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Semi-Supervised Anomaly Detection

Paper content:
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
Typically anomaly detection is treated as an unsupervised learning problem.
In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous.
Semi-supervised approaches to anomaly detection aim to utilize such labeled samples, but most proposed methods are limited to merely including labeled normal samples.
Only a few methods take advantage of labeled anomalies, with existing deep approaches being domain-specific.
In this work we present Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection.
Using an information-theoretic perspective on anomaly detection, we derive a loss motivated by the idea that the entropy of the latent distribution for normal data should be lower than the entropy of the anomalous distribution.
We demonstrate in extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10, along with other anomaly detection benchmark datasets, that our method is on par or outperforms shallow, hybrid, and deep competitors, yielding appreciable performance improvements even when provided with only little labeled data.

assistant: text: We introduce Deep SAD, a deep method for general semi-supervised anomaly detection that especially takes advantage of labeled anomalies.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Data Augmentation Generative Adversarial Networks

Paper content:
Effective training of neural networks requires much data.
In the low-data regime,
parameters are underdetermined, and learnt networks generalise poorly.
Data
Augmentation (Krizhevsky et al., 2012) alleviates this by using existing data
more effectively.
However standard data augmentation produces only limited
plausible alternative data.
Given there is potential to generate a much broader set
of augmentations, we design and train a generative model to do data augmentation.
The model, based on image conditional Generative Adversarial Networks, takes
data from a source domain and learns to take any data item and generalise it
to generate other within-class data items.
As this generative process does not
depend on the classes themselves, it can be applied to novel unseen classes of data.
We show that a Data Augmentation Generative Adversarial Network (DAGAN)
augments standard vanilla classifiers well.
We also show a DAGAN can enhance
few-shot learning systems such as Matching Networks.
We demonstrate these
approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and
VGG-Face data.
In our experiments we can see over 13% increase in accuracy in
the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9%
to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we
observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in
EMNIST (from 59.5% to 61.3%).

assistant: text: Conditional GANs trained to generate data augmented samples of their conditional inputs used to enhance vanilla classification and one shot learning systems such as matching networks and pixel distance
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Approximation capability of neural networks on sets of probability measures and tree-structured data

Paper content:
This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures.
By doing so the work parallels a more then a decade old results on mean-map embedding of probability measures in reproducing kernel Hilbert spaces.
The work has wide practical consequences for multi-instance learning, where it theoretically justifies some recently proposed constructions.
The result is then extended to Cartesian products, yielding universal approximation theorem for tree-structured domains, which naturally occur in data-exchange formats like JSON, XML, YAML, AVRO, and ProtoBuffer.
This has important practical implications, as it enables to automatically create an architecture of neural networks for processing structured data (AutoML paradigms), as demonstrated by an accompanied library for JSON format.

assistant: text: This paper generalizes the universal approximation theorem to real functions on the space of measures.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LSH-SAMPLING BREAKS THE COMPUTATIONAL CHICKEN-AND-EGG LOOP IN ADAPTIVE STOCHASTIC GRADIENT ESTIMATION

Paper content:
Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems.
SGD estimates the gradient by uniform sampling with sample size one.
There have been several other works that suggest faster epoch wise convergence by using weighted non-uniform sampling for better gradient estimates.
Unfortunately, the per-iteration cost of maintaining this adaptive distribution for gradient estimation is more than calculating the full gradient.
As a result, the false impression of faster convergence in iterations leads to slower convergence in time, which we call a chicken-and-egg loop.
In this paper, we break this barrier by providing the first demonstration of a sampling scheme, which leads to superior gradient estimation, while keeping the sampling cost per iteration similar to that of the uniform sampling.
Such an algorithm is possible due to the sampling view of Locality Sensitive Hashing (LSH), which came to light recently.
As a consequence of superior and fast estimation, we reduce the running time of all existing gradient descent algorithms.
We demonstrate the benefits of our proposal on both SGD and AdaGrad.
In this paper, we proposed a novel LSH-based sampler with a reduction to the gradient estimation variance.
We achieved it by sampling with probability proportional to the L 2 norm of the instances gradients leading to an optimal distribution that minimizes the variance of estimation.
More remarkably, LSD is as computationally efficient as SGD but achieves faster convergence not only epoch wise but also time wise.Peilin Zhao and Tong Zhang.
Stochastic optimization with importance sampling for regularized loss minimization.
In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pp. 1-9, 2015.A EPOCH PLOTS AND PROOFS Theorem 3.
Let S be the bucket that sample x m is chosen from in Algorithm 2.
Let p m be the sampling probability associated with sample x m .
Suppose we query a sample with ✓ t .
Then we have an unbiased estimator of the full gradient: DISPLAYFORM0 Proof.
DISPLAYFORM1 Theorem 4.
The Trace of the covariance of our estimator is: DISPLAYFORM2 Proof.

assistant: text: Considers SGD over an objective of the form of a sum over examples of a quadratic loss.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

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

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

assistant: text: A fast optimizer for general applications and large-batch training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CAN ALTQ LEARN FASTER: EXPERIMENTS AND THEORY

Paper content:
Differently from the popular Deep Q-Network (DQN) learning, Alternating Q-learning (AltQ) does not fully fit a target Q-function at each iteration, and is generally known to be unstable and inefficient.
Limited applications of AltQ mostly rely on substantially altering the algorithm architecture in order to improve its performance.
Although Adam appears to be a natural solution, its performance in AltQ has rarely been studied before.
In this paper, we first provide a solid exploration on how well AltQ performs with Adam.
We then take a further step to improve the implementation by adopting the technique of parameter restart.
More specifically, the proposed algorithms are tested on a batch of Atari 2600 games and exhibit superior performance than the DQN learning method.
The convergence rate of the slightly modified version of the proposed algorithms is characterized under the linear function approximation.
To the best of our knowledge, this is the first theoretical study on the Adam-type algorithms in Q-learning.

assistant: text: This paper describes a method to improve the AltQ algorithm by using a combination of an Adam optimizer and regularly restarting the internal parameters of the Adam optimizer.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text

Paper content:
In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops.
These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation.
However, transliterating between older and newer texts is a laborious task when performed manually.
We introduce two related methods to help solve this transliteration problem automatically, given that there were not enough data to train an end-to-end deep learning model.
One approach is implemented, end-to-end, using finite state transducers (FSTs).
The other is a hybrid deep learning approach which approximately composes an FST with a recurrent neural network (RNN).
We find that the hybrid approach outperforms the end-to-end FST by partitioning the original problem into one part that can be modelled by hand, using an FST, and into another part, which is easily solved by an RNN trained on the available data.

assistant: text: A novel, hybrid deep learning approach provides the best solution to a limited-data problem (which is important to the conservation of the Hawaiian language)
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling

Paper content:
Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently.
However, learning from demonstrations often suffers from the covariate shift problem, which results
in cascading errors of the learned policy.
We introduce a notion of conservatively extrapolated value functions, which provably lead to policies with self-correction.
We design an algorithm Value Iteration with Negative Sampling (VINS) that practically learns such value functions with conservative extrapolation.
We show that VINS can correct mistakes of the behavioral cloning policy on simulated robotics benchmark tasks.
We also propose the algorithm of using VINS to initialize a reinforcement learning algorithm, which is shown to outperform prior works in sample efficiency.

assistant: text: An algorithm called value iteration with negative sampling to address the covariate shift problem in imitation learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Characterizing the High-dimensional Bias of Kernel-based Particle Inference Algorithms

Paper content:
Particle-based inference algorithm is a promising method to efficiently generate samples for an intractable target distribution by iteratively updating a set of particles.
As a noticeable example, Stein variational gradient descent (SVGD) provides a deterministic and computationally efficient update, but it is known to underestimate the variance in high dimensions, the mechanism of which is poorly understood.
In this work we explore a connection between SVGD and MMD-based inference algorithm via Stein's lemma.
By comparing the two update rules, we identify the source of bias in SVGD as a combination of high variance and deterministic bias, and empirically demonstrate that the removal of either factors leads to accurate estimation of the variance.
In addition, for learning high-dimensional Gaussian target, we analytically derive the converged variance for both algorithms, and confirm that only SVGD suffers from the "curse of dimensionality".

assistant: text: Analyze the underlying mechanisms of variance collapse of SVGD in high dimensions.
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: We introduce a black box algorithm for repeated optimization of compounds using a translation framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Learning with Domain Adaptation for Few-Shot Learning under Domain Shift

Paper content:
Few-Shot Learning (learning with limited labeled data) aims to overcome the limitations of traditional machine learning approaches which require thousands of labeled examples to train an effective model.
Considered as a hallmark of human intelligence, the community has recently witnessed several contributions on this topic, in particular through meta-learning, where a model learns how to learn an effective model for few-shot learning.
The main idea is to acquire prior knowledge from a set of training tasks, which is then used to perform (few-shot) test tasks.
Most existing work assumes that both training and test tasks are drawn from the same distribution, and a large amount of labeled data is available in the training tasks.
This is a very strong assumption which restricts the usage of meta-learning strategies in the real world where ample training tasks following the same distribution as test tasks may not be available.
In this paper, we propose a novel meta-learning paradigm wherein a few-shot learning model is learnt, which simultaneously overcomes domain shift between the train and test tasks via adversarial domain adaptation.
We demonstrate the efficacy the proposed method through extensive experiments.

assistant: text: The authors proposed meta domain adaptation to address domain shift scenario in meta learning setup, demonstrating performance improvements in several experiments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Difference-Seeking Generative Adversarial Network

Paper content:
We propose a novel algorithm, Difference-Seeking Generative Adversarial Network (DSGAN), developed from traditional GAN.
DSGAN considers the scenario that the training samples of target distribution, $p_{t}$, are difficult to collect.
Suppose there are two distributions  $p_{\bar{d}}$ and $p_{d}$ such that the density of the target distribution can be the differences between the densities of $p_{\bar{d}}$ and $p_{d}$.
We show how to learn the target distribution $p_{t}$ only via samples from $p_{d}$ and $p_{\bar{d}}$ (relatively easy to obtain).
DSGAN has the flexibility to produce samples from various target distributions (e.g. the out-of-distribution).
Two key applications, semi-supervised learning and adversarial training, are taken as examples to validate the effectiveness of DSGAN.
We also provide theoretical analyses about the convergence of DSGAN.
In machine learning, how to learn a probability distribution is usually conducted in a unsupervised learning manner.
Generative approaches are developed for learning data distribution from its samples and thereafter produce novel and high-dimensional samples from learned distributions, such as image and speech synthesis BID18 ).
The state-of-the-art approaches is so-called Generative Adversarial Networks (GAN) BID6 ).
GAN produces sharp images based on a game-theoretic framework, but can be tricky and unstable to train due to multiple interacting losses.
Specifically, GAN consists of two functions: generator and discriminator.
Both functions are represented as parameterized neural networks.
The discriminator network is trained to classify whether or not inputs belong to real data or fake data created by the generator.
The generator learns to map a sample from a latent space to some distribution to increase the classification errors of discriminator.
GAN corresponds to a minimax two-player game, which ends if the generator actually learns the real data distribution.
The generator is of main interest because the discriminator will be unable to differentiate between both distributions once the generator has been trained well.
In this paper, we propose DSGAN that can produce samples from the target distribution based on the assumption that the density of target distribution can be the difference between the densities of any two distributions.
DSGAN is useful in the environment when the samples from the target distribution are more difficult to collect than those from the two known distributions.
We demonstrate that DSGAN is really applicable to, for example, semi-supervised learning and adversarial training.
Empirical and theoretical results are provided to validate the effectiveness of DSGAN.
Finally, because DSGAN is developed based on traditional GAN, it is easy to extend any improvements of traditional GAN to DSGAN.
We complete this proof.

assistant: text: We proposed "Difference-Seeking Generative Adversarial Network" (DSGAN) model to learn the target distribution which is hard to collect training data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Residual Energy-Based Models for Text Generation

Paper content:
Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation.
The dominant parametric approach is based on locally normalized models which predict one word at a time.
While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generation process.
In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level.
In order to make training tractable, we first work in the residual of a pretrained locally normalized language model and second we train using noise contrastive estimation.
Furthermore, since the EBM works at the sequence level, we can leverage pretrained bi-directional contextual representations, such as BERT and RoBERTa.
Our experiments on two large language modeling datasets show that residual EBMs yield lower perplexity compared to locally normalized baselines.
Moreover, generation via importance sampling is very efficient and of higher quality than the baseline models according to human evaluation.

assistant: text: A proposed Residual Energy-based Model (EBM) for text generation which operates at the sentence level, and can therefore leverage BERT, and achieves lower perplexity and is preferred by human evaluation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Higher-order Weighted Graph Convolutional Networks

Paper content:
Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.
Existing approaches that deal with the higher-order neighbors tend to take advantage of adjacency matrix power.
In this paper, we assume a seemly trivial condition that the higher-order neighborhood information may be similar to that of the first-order neighbors.
Accordingly, we present an unsupervised approach to describe such similarities and learn the weight matrices of higher-order neighbors automatically through Lasso that minimizes the feature loss between the first-order and higher-order neighbors, based on which we formulate the new convolutional filter for GCN to learn the better node representations.
Our model, called higher-order weighted GCN (HWGCN), has achieved the state-of-the-art results on a number of node classification tasks over Cora, Citeseer and Pubmed datasets.

assistant: text: We propose HWGCN to mix the relevant neighborhood information at different orders to better learn node representations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improved Adversarial Image Captioning

Paper content:
In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions.
We investigate the viability of two discrete GAN training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST.

assistant: text: An improved GAN model for image captioning that proposes a context-aware LSTM captioner, introduces a stronger co-attentive discriminator with better performance, and uses SCST for GAN training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised  one-to-many image translation

Paper content:
We perform completely unsupervised one-sided image to image translation between a source domain $X$ and a target domain $Y$ such that we preserve relevant underlying shared semantics (e.g., class, size, shape, etc).
In particular, we are interested in a more difficult case than those typically addressed in the literature, where the source and target are ``far" enough that reconstruction-style or pixel-wise approaches fail.
We argue that transferring (i.e., \emph{translating}) said relevant information should involve both discarding source domain-specific information while incorporate target domain-specific information, the latter of which we model with a noisy prior distribution.
In order to avoid the degenerate case where the generated samples are only explained by the prior distribution, we propose to minimize an estimate of the mutual information between the generated sample and the sample from the prior distribution.
We discover that the architectural choices are an important factor to consider in order to preserve the shared semantic between $X$ and $Y$.
We show state of the art results on the MNIST to SVHN task for unsupervised image to image translation.

assistant: text: This paper formulates the problem of unsupervised one-to-many image translation and addresses the problem by minimizing the mutual information.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Simultaneous Classification and Out-of-Distribution Detection Using Deep Neural Networks

Paper content:
Deep neural networks have achieved great success in classiﬁcation tasks during the last years.
However, one major problem to the path towards artiﬁcial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classiﬁcation algorithms assume that all classes are known prior to the training stage.
In this work, we propose a methodology for training a neural network that allows it to efﬁciently detect out-of-distribution (OOD) examples without compromising much of its classiﬁcation accuracy on the test examples from known classes.
Based on the Outlier Exposure (OE) technique, we propose a novel loss function that achieves state-of-the-art results in out-of-distribution detection with OE both on image and text classiﬁcation tasks.
Additionally, the way this method was constructed makes it suitable for training any classiﬁcation algorithm that is based on Maximum Likelihood methods.

assistant: text: This paper tackles the problems of out-of-distribution detection and model calibration by adapting the loss function of the Outlier Exposure technique, with results demonstrating increased performance over OE on vision and text benchmarks and improved model calibration.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints

Paper content:
In most practical settings and theoretical analyses, one assumes that a model can be trained until convergence.
However, the growing complexity of machine learning datasets and models may violate such assumptions.
Indeed, current approaches for hyper-parameter tuning and neural architecture search tend to be limited by practical resource constraints.
Therefore, we introduce a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e., budgeted training.
We analyze the following problem: "given a dataset, algorithm, and fixed resource budget, what is the best achievable performance?"
We focus on the number of optimization iterations as the representative resource.
Under such a setting, we show that it is critical to adjust the learning rate schedule according to the given budget.
Among budget-aware learning schedules, we find simple linear decay to be both robust and high-performing.
We support our claim through extensive experiments with state-of-the-art models on ImageNet (image classification), Kinetics (video classification), MS COCO (object detection and instance segmentation), and Cityscapes (semantic segmentation).
We also analyze our results and find that the key to a good schedule is budgeted convergence, a phenomenon whereby the gradient vanishes at the end of each allowed budget.
We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.

assistant: text: Introduce a formal setting for budgeted training and propose a budget-aware linear learning rate schedule
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Prediction Under Uncertainty with Error Encoding Networks

Paper content:
In this work we introduce a new framework for performing temporal predictions
in the presence of uncertainty.
It is based on a simple idea of disentangling com-
ponents of the future state which are predictable from those which are inherently
unpredictable, and encoding the unpredictable components into a low-dimensional
latent variable which is fed into the forward model.
Our method uses a simple su-
pervised training objective which is fast and easy to train.
We evaluate it in the
context of video prediction on multiple datasets and show that it is able to consi-
tently generate diverse predictions without the need for alternating minimization
over a latent space or adversarial training.

assistant: text: The paper proposes a model for prediction under uncertainty where they separate out deterministic component prediction and uncertain component prediction.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Construction of Macro Actions for Deep Reinforcement Learning

Paper content:
Conventional deep reinforcement learning typically determines an appropriate primitive action at each timestep, which requires enormous amount of time and effort for learning an effective policy, especially in large and complex environments.
To deal with the issue fundamentally, we incorporate macro actions, defined as sequences of primitive actions, into the primitive action space to form an augmented action space.
The problem lies in how to find an appropriate macro action to augment the primitive action space.
The agent using a proper augmented action space is able to jump to a farther state and thus speed up the exploration process as well as facilitate the learning procedure.
In previous researches, macro actions are developed by mining the most frequently used action sequences or repeating previous actions.
However, the most frequently used action sequences are extracted from a past policy, which may only reinforce the original behavior of that policy.
On the other hand, repeating actions may limit the diversity of behaviors of the agent.
Instead, we propose to construct macro actions by a genetic algorithm, which eliminates the dependency of the macro action derivation procedure from the past policies of the agent.
Our approach appends a macro action to the primitive action space once at a time and evaluates whether the augmented action space leads to promising performance or not.
We perform extensive experiments and show that the constructed macro actions are able to speed up the learning process for a variety of deep reinforcement learning methods.
Our experimental results also demonstrate that the macro actions suggested by our approach are transferable among deep reinforcement learning methods and similar environments.
We further provide a comprehensive set of ablation analysis to validate our methodology.

assistant: text: This paper proposes a generic algorithm for constructing macro actions for deep reinforcement learning by appending a macro action to the primitive action space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: AlgoNet: $C^\infty$ Smooth Algorithmic Neural Networks for Solving Inverse Problems

Paper content:
Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems.
On the other hand, for many problems, classic algorithms exist, which typically exceed the accuracy and stability of neural networks.
To combine these two concepts, we present a new kind of neural networks—algorithmic neural networks (AlgoNets).
These networks integrate smooth versions of classic algorithms into the topology of neural networks.
Our novel reconstructive adversarial network (RAN) enables solving inverse problems without or with only weak supervision.

assistant: text: Solving inverse problems by using smooth approximations of the forward algorithms to train the inverse models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Model-Agnostic Feature Selection with Additional Mutual Information

Paper content:
Answering questions about data can require understanding what parts of an input X influence the response Y. Finding such an understanding can be built by testing relationships between variables through a machine learning model.
For example, conditional randomization tests help determine whether a variable relates to the response given the rest of the variables.
However, randomization tests require users to specify test statistics.
We formalize a class of proper test statistics that are guaranteed to select a feature when it provides information about the response even when the rest of the features are known.
We show that f-divergences provide a broad class of proper test statistics.
In the class of f-divergences, the KL-divergence yields an easy-to-compute proper test statistic that relates to the AMI.
Questions of feature importance can be asked at the level of an individual sample.
We show that estimators from the same AMI test can also be used to find important features in a particular instance.
We provide an example to show that perfect predictive models are insufficient for instance-wise feature selection.
We evaluate our method on several simulation experiments, on a genomic dataset, a clinical dataset for hospital readmission, and on a subset of classes in ImageNet.
Our method outperforms several baselines in various simulated datasets, is able to identify biologically significant genes, can select the most important predictors of a hospital readmission event, and is able to identify distinguishing features in an image-classification task.

assistant: text: This paper addresses the problem of finding useful features in an input that are dependent on a response variable even when conditioning on all other input variables.