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

Title: Policy Transfer with Strategy Optimization

Paper content:
Computer simulation provides an automatic and safe way for training robotic control
policies to achieve complex tasks such as locomotion.
However, a policy
trained in simulation usually does not transfer directly to the real hardware due
to the differences between the two environments.
Transfer learning using domain
randomization is a promising approach, but it usually assumes that the target environment
is close to the distribution of the training environments, thus relying
heavily on accurate system identification.
In this paper, we present a different
approach that leverages domain randomization for transferring control policies to
unknown environments.
The key idea that, instead of learning a single policy in
the simulation, we simultaneously learn a family of policies that exhibit different
behaviors.
When tested in the target environment, we directly search for the best
policy in the family based on the task performance, without the need to identify
the dynamic parameters.
We evaluate our method on five simulated robotic control
problems with different discrepancies in the training and testing environment
and demonstrate that our method can overcome larger modeling errors compared
to training a robust policy or an adaptive policy.

assistant: text: We propose a policy transfer algorithm that can overcome large and challenging discrepancies in the system dynamics such as latency, actuator modeling error, etc.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Universal Source-Free Domain Adaptation

Paper content:
There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift.
Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA).
Furthermore, almost all the prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for incremental, real-time adaptation.
Devoid of such highly impractical assumptions, we propose a novel two-stage learning process.
Initially, in the procurement-stage, the objective is to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift.
To achieve this, we enhance the model’s ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework.
Subsequently, in the deployment-stage, the objective is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples.
To achieve this, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighing mechanism, named as Source Similarity Metric (SSM).
A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches.

assistant: text: A novel unsupervised domain adaptation paradigm - performing adaptation without accessing the source data ('source-free') and without any assumption about the source-target category-gap ('universal').
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Advantages and Challenges of Using AI Planning in Cloud Migration

Paper content:
Cloud Migration transforms customer’s data, application and services from original IT platform to one or more cloud en- vironment, with the goal of improving the performance of the IT system while reducing the IT management cost.
The enterprise level Cloud Migration projects are generally com- plex, involves dynamically planning and replanning various types of transformations for up to 10k endpoints.
Currently the planning and replanning in Cloud Migration are generally done manually or semi-manually with heavy dependency on the migration expert’s domain knowledge, which takes days to even weeks for each round of planning or replanning.
As a result, automated planning engine that is capable of gener- ating high quality migration plan in a short time is particu- larly desirable for the migration industry.
In this short paper, we briefly introduce the advantages of using AI planning in Cloud Migration, a preliminary prototype, as well as the challenges the requires attention from the planning and scheduling society.

assistant: text: In this short paper, we briefly introduce the advantages of using AI planning in Cloud Migration, a preliminary prototype, as well as the chal- lenges the requires attention from the planning and schedul- ing society.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets

Paper content:
Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks.
Despite its success as a defense mechanism, adversarial training fails to generalize well to unperturbed test set.
We hypothesize that this poor generalization is a consequence of adversarial training with uniform perturbation radius around every training sample.
Samples close to decision boundary can be morphed into a different class under a small perturbation budget, and enforcing large margins around these samples produce poor decision boundaries that generalize poorly.
Motivated by this hypothesis, we propose instance adaptive adversarial training -- a technique that enforces sample-specific perturbation margins around every training sample.
We show that using our approach, test accuracy on unperturbed samples improve with a marginal drop in robustness.
Extensive experiments on CIFAR-10, CIFAR-100 and Imagenet datasets demonstrate the effectiveness of our proposed approach.

assistant: text: Instance adaptive adversarial training for improving robustness-accuracy tradeoff
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reinforcement Learning with Perturbed Rewards

Paper content:
Recent studies have shown the vulnerability of reinforcement learning (RL) models in noisy settings.
The sources of noises differ across scenarios.
For instance, in practice, the observed reward channel is often subject to noise (e.g., when observed rewards are collected through sensors), and thus observed rewards may not be credible as a result.
Also, in applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors.
In this paper, we consider noisy RL problems where observed rewards by RL agents are generated with a reward confusion matrix.
We call such observed rewards as perturbed rewards.
We develop an unbiased reward estimator aided robust RL framework that enables RL agents to learn in noisy environments while observing only perturbed rewards.
Our framework draws upon approaches for supervised learning with noisy data.
The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards.
We prove the convergence and sample complexity of our approach.
Extensive experiments on different DRL platforms show that policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines.
For instance, the state-of-the-art PPO algorithm is able to obtain 67.5% and 46.7% improvements in average on five Atari games, when the error rates are 10% and 30% respectively.

assistant: text: A new approach for learning with noisy rewards in reinforcement learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning

Paper content:
Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples.
Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings.
Existing approaches to finding efficient exploration strategies add auxiliary objectives to promote exploration by the pre-update policy, however, this makes the adaptation using a few gradient steps difficult as the pre-update (exploration) and post-update (exploitation) policies are quite different.
Instead, we propose to explicitly model a separate exploration policy for the task distribution.
Having two different policies gives more flexibility in training the exploration policy and also makes adaptation to any specific task easier.
We show that using self-supervised or supervised learning objectives for adaptation stabilizes the training process and also demonstrate the superior performance of our model compared to prior works in this domain.

assistant: text: We propose to use a separate exploration policy to collect the pre-adaptation trajectories in MAML. We also show that using a self-supervised objective in the inner loop leads to more stable training and much better performance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised Hierarchical Video Prediction

Paper content:
Much recent research has been devoted to video prediction and generation,  but mostly for short-scale time horizons.
The hierarchical video prediction method by Villegas et al. (2017) is an example of a state of the art method for long term video prediction.
However, their method has limited applicability in practical settings as it requires a ground truth pose (e.g.,  poses of joints of a human) at training time.
This paper presents a long term hierarchical video prediction model that does not have such a restriction.
We show that the network learns its own higher-level structure (e.g., pose equivalent hidden variables) that works better in cases where the ground truth pose does not fully capture all of the information needed to  predict  the  next  frame.
This  method  gives  sharper  results  than  other  video prediction methods which do not require a ground truth pose, and its efficiency is shown on the Humans 3.6M and Robot Pushing datasets.

assistant: text: We show ways to train a hierarchical video prediction model without needing pose labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sparsity Emerges Naturally in Neural Language Models

Paper content:
Concerns about interpretability, computational resources, and principled inductive priors have motivated efforts to engineer sparse  neural  models for NLP tasks.
If sparsity is important for NLP, might well-trained neural models naturally become roughly sparse?
Using the Taxi-Euclidean norm to measure sparsity, we find that frequent input words are associated with concentrated or sparse activations, while frequent target words are associated with dispersed activations but concentrated gradients.
We find that  gradients associated with function words are more concentrated than the gradients of content words, even controlling for word frequency.
Researchers in NLP have long relied on engineering features to reflect the sparse structures underlying language.
Modern deep learning methods promised to relegate this practice to history, but have not eliminated the interest in sparse modeling for NLP.
Along with concerns about computational resources BID0 BID12 and interpretability BID10 BID21 , human intuitions continue to motivate sparse representations of language.
For example, some work applies assumptions of sparsity to model latent hard categories such as syntactic dependencies BID14 or phonemes BID1 .
BID13 found that a sparse attention mechanism outperformed dense methods on some NLP tasks; BID11 found sparsified versions of LMs that outperform dense originals.
Attempts to engineer sparsity rest on an unstated assumption that it doesn't arise naturally when neural models are learned.
Is this true?Using
a simple measure of sparsity, we analyze how it arises in different layers of a neural language model in relation to word frequency. We show
that the sparsity of a word representation increases with exposure to that word during training. We also
find evidence of syntactic learning: gradient updates in backpropagation depend on whether a word's part of speech is open or closed class, even controlling for word frequency.

assistant: text: We study the natural emergence of sparsity in the activations and gradients for some layers of a dense LSTM language model, over the course of training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: How Does Learning Rate Decay Help Modern Neural Networks?

Paper content:
Learning rate decay (lrDecay) is a \emph{de facto} technique for training modern neural networks.
It starts with a large learning rate and then decays it multiple times.
It is empirically observed to help both optimization and generalization.
Common beliefs in how lrDecay works come from the optimization analysis of (Stochastic) Gradient Descent:
1) an initially large learning rate accelerates training or helps the network escape spurious local minima;
2) decaying the learning rate helps the network converge to a local minimum and avoid oscillation.
Despite the popularity of these common beliefs, experiments suggest that they are insufficient in explaining the general effectiveness of lrDecay in training modern neural networks that are deep, wide, and nonconvex.
We provide another novel explanation: an initially large learning rate suppresses the network from memorizing noisy data while decaying the learning rate improves the learning of complex patterns.
The proposed explanation is validated on a carefully-constructed dataset with tractable pattern complexity.
And its implication, that additional patterns learned in later stages of lrDecay are more complex and thus less transferable, is justified in real-world datasets.
We believe that this alternative explanation will shed light into the design of better training strategies for modern neural networks.

assistant: text: We provide another novel explanation of learning rate decay: an initially large learning rate suppresses the network from memorizing noisy data while decaying the learning rate improves the learning of complex patterns.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory

Paper content:
Cortical neurons process and integrate information on multiple timescales.
In addition, these timescales or temporal receptive fields display functional and hierarchical organization.
For instance, areas important for working memory (WM), such as prefrontal cortex, utilize neurons with stable temporal receptive fields and long timescales to support reliable representations of stimuli.
Despite of the recent advances in experimental techniques, the underlying mechanisms for the emergence of neuronal timescales long enough to support WM are unclear and challenging to investigate experimentally.
Here, we demonstrate that spiking recurrent neural networks (RNNs) designed to perform a WM task reproduce previously observed experimental findings and that these models could be utilized in the future to study how neuronal timescales specific to WM emerge.

assistant: text: Spiking recurrent neural networks performing a working memory task utilize long heterogeneous timescales, strikingly similar to those observed in prefrontal cortex.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Double Neural Counterfactual Regret Minimization

Paper content:
Counterfactual regret minimization (CFR) is a fundamental and effective technique for solving Imperfect Information Games (IIG).
However, the original CFR algorithm only works for discrete states and action spaces, and the resulting strategy is maintained as a tabular representation.
Such tabular representation limits the method from being directly applied to large games.
In this paper, we propose a double neural representation for the IIGs, where one neural network represents the cumulative regret, and the other represents the average strategy.
Such neural representations allow us to avoid manual game abstraction and carry out end-to-end optimization.
To make the learning efficient, we also developed several novel techniques including a robust sampling method and a mini-batch Monte Carlo Counterfactual Regret Minimization (MCCFR) method, which may be of independent interests.
Empirically, on games tractable to tabular approaches, neural strategies trained with our algorithm converge comparably to their tabular counterparts, and significantly outperform those based on deep reinforcement learning.
On extremely large games with billions of decision nodes, our approach achieved strong performance while using hundreds of times less memory than the tabular CFR.
On head-to-head matches of hands-up no-limit texas hold'em, our neural agent beat the strong agent ABS-CFR by $9.8\pm4.1$ chips per game.
It's a successful application of neural CFR in large games.

assistant: text: We proposed a double neural framework to solve large-scale imperfect information game.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Investigation of using disentangled and interpretable representations with language conditioning for cross-lingual voice conversion

Paper content:
We study the problem of cross-lingual voice conversion in non-parallel speech corpora and one-shot learning setting.
Most prior work require either parallel speech corpora or enough amount of training data from a target speaker.
However, we convert an arbitrary sentences of an arbitrary source speaker to target speaker's given only one target speaker training utterance.
To achieve this, we formulate the problem as learning disentangled speaker-specific and context-specific representations and follow the idea of [1] which uses Factorized Hierarchical Variational Autoencoder (FHVAE).
After training FHVAE on multi-speaker training data, given arbitrary source and target speakers' utterance, we estimate those latent representations and then reconstruct the desired utterance of converted voice to that of target speaker.
We use multi-language speech corpus to learn a universal model that works for all of the languages.
We investigate the use of a one-hot language embedding to condition the model on the language of the utterance being queried and show the effectiveness of the approach.
We conduct voice conversion experiments with varying size of training utterances and it was able to achieve reasonable performance with even just one training utterance.
We also investigate the effect of using or not using the language conditioning.
Furthermore, we visualize the embeddings of the different languages and sexes.
Finally, in the subjective tests, for one language and cross-lingual voice conversion, our approach achieved moderately better or comparable results compared to the baseline in speech quality and similarity.

assistant: text: We use a Variational Autoencoder to separate style and content, and achieve voice conversion by modifying style embedding and decoding. We investigate using a multi-language speech corpus and investigate its effects.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Exploration Strategy for Self-Supervised Imitation Learning

Paper content:
We present an adversarial exploration strategy, a simple yet effective imitation learning scheme that incentivizes exploration of an environment without any extrinsic reward or human demonstration.
Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other.
The former collects training samples for the latter, and its objective is to maximize the error of the latter.
The latter is trained with samples collected by the former, and generates rewards for the former when it fails to predict the actual action taken by the former.
In such a competitive setting, the DRL agent learns to generate samples that the inverse dynamics model fails to predict correctly, and the inverse dynamics model learns to adapt to the challenging samples.
We further propose a reward structure that ensures the DRL agent collects only moderately hard samples and not overly hard ones that prevent the inverse model from imitating effectively.
We evaluate the effectiveness of our method on several OpenAI gym robotic arm and hand manipulation tasks against a number of baseline models.
Experimental results show that our method is comparable to that directly trained with expert demonstrations, and superior to the other baselines even without any human priors.

assistant: text: A simple yet effective imitation learning scheme that incentivizes exploration of an environment without any extrinsic reward or human demonstration.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Overfitting Detection of Deep Neural Networks without a Hold Out Set

Paper content:
Overfitting is an ubiquitous problem in neural network training and usually mitigated using a holdout data set.
Here we challenge this rationale and investigate criteria for overfitting without using a holdout data set.
Specifically, we train a model for a fixed number of epochs multiple times with varying fractions of randomized labels and for a range of regularization strengths.
A properly trained model should not be able to attain an accuracy greater than the fraction of properly labeled data points.
Otherwise the model overfits.
We introduce two criteria for detecting overfitting and one to detect underfitting.
We analyze early stopping, the regularization factor, and network depth.
In safety critical applications we are interested in models and parameter settings which perform well and are not likely to overfit.
The methods of this paper allow characterizing and identifying such models.

assistant: text: We introduce and analyze several criteria for detecting overfitting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ASYNCHRONOUS MULTI-AGENT GENERATIVE ADVERSARIAL IMITATION LEARNING

Paper content:
Imitation learning aims to inversely learn a policy from expert demonstrations, which has been extensively studied in the literature for both single-agent setting with Markov decision process (MDP) model, and multi-agent setting with Markov game (MG) model.
However, existing approaches for general multi-agent Markov games are not applicable to multi-agent extensive Markov games, where agents make asynchronous decisions following a certain order, rather than simultaneous decisions.
We propose a novel framework for asynchronous multi-agent generative adversarial imitation learning (AMAGAIL) under general extensive Markov game settings, and the learned expert policies are proven to guarantee subgame perfect equilibrium (SPE), a more general and stronger equilibrium than Nash equilibrium (NE).
The experiment results demonstrate that compared to state-of-the-art baselines, our AMAGAIL model can better infer the policy of each expert agent using their demonstration data collected from asynchronous decision-making scenarios (i.e., extensive Markov games).

assistant: text: This paper extends the multi-agent generative adversarial imitation learning to extensive-form Markov games.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Long Term Memory Network for Combinatorial Optimization Problems

Paper content:
This paper introduces a framework for solving combinatorial optimization problems by learning from input-output examples of optimization problems.
We introduce a new memory augmented neural model in which the memory is not resettable (i.e the information stored in the memory after processing an input example is kept for the next seen examples).
We used deep reinforcement learning to train a memory controller agent to store useful memories.
Our model was able to outperform hand-crafted solver on Binary Linear Programming (Binary LP).
The proposed model is tested on different Binary LP instances with large number of variables (up to 1000 variables) and constrains (up to 700 constrains).

assistant: text: We propose a memory network model to solve Binary LP instances where the memory information is perseved for long-term use.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Closer Look at Few-shot Classification

Paper content:
Few-shot classiﬁcation aims to learn a classiﬁer to recognize unseen classes during training with limited labeled examples.
While signiﬁcant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difﬁcult.
In this paper, we present
1) a consistent comparative analysis of several representative few-shot classiﬁcation algorithms, with results showing that deeper backbones signiﬁcantly reduce the gap across methods including the baseline,
2) a slightly modiﬁed baseline method that surprisingly achieves competitive performance when compared with the state-of-the-art on both the mini-ImageNet and the CUB datasets, and
3) a new experimental setting for evaluating the cross-domain generalization ability for few-shot classiﬁcation algorithms.
Our results reveal that reducing intra-class variation is an important factor when the feature backbone is shallow, but not as critical when using deeper backbones.
In a realistic, cross-domain evaluation setting, we show that a baseline method with a standard ﬁne-tuning practice compares favorably against other state-of-the-art few-shot learning algorithms.

assistant: text: A detailed empirical study in few-shot classification that revealing challenges in standard evaluation setting and showing a new direction.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Knossos: Compiling AI with AI

Paper content:
Machine learning workloads are often expensive to train, taking weeks to converge.
The current generation of frameworks relies on custom back-ends in order to achieve efficiency, making it impractical to train models on less common hardware where no such back-ends exist.
Knossos builds on recent work that avoids the need for hand-written libraries, instead compiles machine learning models in much the same way one would compile other kinds of software.
In order to make the resulting code efficient, the Knossos complier directly optimises the abstract syntax tree of the program.
However in contrast to traditional compilers that employ hand-written optimisation passes, we take a rewriting approach driven by the $A^\star$ search algorithm and a learn value function that evaluates future potential cost reduction of taking various rewriting actions to the program.
We show that Knossos can automatically learned optimisations that past compliers had to implement by hand.
Furthermore, we demonstrate that Knossos can achieve wall time reduction compared to a hand-tuned compiler on a suite of machine learning programs, including basic linear algebra and convolutional networks.
The Knossos compiler has minimal dependencies and can be used on any architecture that supports a \Cpp toolchain.
Since cost model the proposed algorithm optimises can be tailored to a particular hardware architecture, the proposed approach can potentially applied to a variety of hardware.

assistant: text: We combine A* search with reinforcement learning to speed up machine learning code
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation

Paper content:
Neural sequence generation is commonly approached by using maximum- likelihood (ML) estimation or reinforcement learning (RL).
However, it is known that they have their own shortcomings; ML presents training/testing discrepancy, whereas RL suffers from sample inefficiency.
We point out that it is difficult to resolve all of the shortcomings simultaneously because of a tradeoff between ML and RL.
In order to counteract these problems, we propose an objective function for sequence generation using α-divergence, which leads to an ML-RL integrated method that exploits better parts of ML and RL.
We demonstrate that the proposed objective function generalizes ML and RL objective functions because it includes both as its special cases (ML corresponds to α → 0 and RL to α → 1).
We provide a proposition stating that the difference between the RL objective function and the proposed one monotonically decreases with increasing α.
Experimental results on machine translation tasks show that minimizing the proposed objective function achieves better sequence generation performance than ML-based methods.

assistant: text: Propose new objective function for neural sequence generation which integrates ML-based and RL-based objective functions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Predictive Coding, Variational Autoencoders, and Biological Connections

Paper content:
Predictive coding, within theoretical neuroscience, and variational autoencoders, within machine learning, both involve latent Gaussian models and variational inference.
While these areas share a common origin, they have evolved largely independently.
We outline connections and contrasts between these areas, using their relationships to identify new parallels between machine learning and neuroscience.
We then discuss specific frontiers at this intersection: backpropagation, normalizing flows, and attention, with mutual benefits for both fields.

assistant: text: connections between predictive coding and VAEs + new frontiers
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Matrix Product Operator Restricted Boltzmann Machines

Paper content:
A restricted Boltzmann machine (RBM) learns a probabilistic distribution over its input samples and has numerous uses like dimensionality reduction, classification and generative modeling.
Conventional RBMs accept vectorized data that dismisses potentially important structural information in the original tensor (multi-way) input.
Matrix-variate and tensor-variate RBMs, named MvRBM and TvRBM, have been proposed but are all restrictive by construction.
This work presents the matrix product operator RBM (MPORBM) that utilizes a tensor network generalization of Mv/TvRBM, preserves input formats in both the visible and hidden layers, and results in higher expressive power.
A novel training algorithm integrating contrastive divergence and an alternating optimization procedure is also developed.

assistant: text: Propose a general tensor-based RBM model which can compress the model greatly at the same keep a strong model expression capacity
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Learning of General Transformations for Data Augmentation

Paper content:
Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, especially with a limited training dataset.
In images, DA is usually based on heuristic transformations, like geometric or color transformations.
Instead of using predefined transformations, our work learns data augmentation directly from the training data by learning to transform images with an encoder-decoder architecture combined with a spatial transformer network.
The transformed images still belong to the same class, but are new, more complex samples for the classifier.
Our experiments show that our approach is better than previous generative data augmentation methods, and comparable to predefined transformation methods when training an image classifier.

assistant: text: Automatic Learning of data augmentation using a GAN based architecture to improve an image classifier
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Implicit Bias of Depth: How Incremental Learning Drives Generalization

Paper content:
A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity.
We formally define the notion of incremental learning dynamics and derive the conditions on depth and initialization for which this phenomenon arises in deep linear models.
Our main theoretical contribution is a dynamical depth separation result, proving that while shallow models can exhibit incremental learning dynamics, they require the initialization to be exponentially small for these dynamics to present themselves.
However, once the model becomes deeper, the dependence becomes polynomial and incremental learning can arise in more natural settings.
We complement our theoretical findings by experimenting with deep matrix sensing, quadratic neural networks and with binary classification using diagonal and convolutional linear networks, showing all of these models exhibit incremental learning.

assistant: text: We study the sparsity-inducing bias of deep models, caused by their learning dynamics.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction

Paper content:
Network embedding (NE) methods aim to learn low-dimensional representations of network nodes as vectors, typically in Euclidean space.
These representations are then used for a variety of downstream prediction tasks.
Link prediction is one of the most popular choices for assessing the performance of NE methods.
However, the complexity of link prediction requires a carefully designed evaluation pipeline to provide consistent, reproducible and comparable results.
We argue this has not been considered sufficiently in recent works.
The main goal of this paper is to overcome difficulties associated with evaluation pipelines and reproducibility of results.
We introduce EvalNE, an evaluation framework to transparently assess and compare the performance of NE methods on link prediction.
EvalNE provides automation and abstraction for tasks such as hyper-parameter tuning, model validation, edge sampling, computation of edge embeddings and model validation.
The framework integrates efficient procedures for edge and non-edge sampling and can be used to easily evaluate any off-the-shelf embedding method.
The framework is freely available as a Python toolbox.
Finally, demonstrating the usefulness of EvalNE in practice, we conduct an empirical study in which we try to replicate and analyse experimental sections of several influential papers.

assistant: text: In this paper we introduce EvalNE, a Python toolbox for automating the evaluation of network embedding methods on link prediction and ensuring the reproducibility of results.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Large-scale Cloze Test Dataset Designed by Teachers

Paper content:
Cloze test is widely adopted in language exams to evaluate students' language proficiency.
In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH in which the questions were used in middle-school and high-school language exams.
With the missing blanks carefully created by teachers and candidate choices purposely designed to be confusing, CLOTH requires a deeper language understanding and a wider attention span than previous automatically generated cloze datasets.
We show humans outperform dedicated designed baseline models by a significant margin, even when the model is trained on sufficiently large external data.
We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending a long-term context to be the key bottleneck.
In addition, we find that human-designed data leads to a larger gap between the model's performance and human performance when compared to automatically generated data.

assistant: text: A cloze test dataset designed by teachers to assess language proficiency
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Factorized Multimodal Transformer for Multimodal Sequential Learning

Paper content:
The complex world around us is inherently multimodal and sequential (continuous).
Information is scattered across different modalities and requires multiple continuous sensors to be captured.
As machine learning leaps towards better generalization to real world, multimodal sequential learning becomes a fundamental research area.
Arguably,  modeling arbitrarily distributed spatio-temporal dynamics within and across modalities is the biggest challenge in this research area.
In this paper, we present a new transformer model, called the Factorized Multimodal Transformer (FMT) for multimodal sequential learning.
FMT inherently models the intramodal and intermodal (involving two or more modalities) dynamics within its multimodal input in a factorized manner.
The proposed factorization allows for increasing the number of self-attentions to better model the multimodal phenomena at hand; without encountering difficulties during training (e.g. overfitting) even on relatively low-resource setups.
All the attention mechanisms within FMT have a full time-domain receptive field which allows them to asynchronously capture long-range multimodal dynamics.
In our experiments we focus on datasets that contain the three commonly studied modalities of language, vision and acoustic.
We perform a wide range of experiments, spanning across 3 well-studied datasets and 21 distinct labels.
FMT shows superior performance over previously proposed models, setting new state of the art in  the studied datasets.

assistant: text: A multimodal transformer for multimodal sequential learning, with strong empirical results on multimodal language metrics such as multimodal sentiment analysis, emotion recognition and personality traits recognition.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Networks for Principal Component Analysis: A New Loss Function Provably Yields Ordered Exact Eigenvectors 

Paper content:
In this paper, we propose a new loss function for performing principal component analysis (PCA) using linear autoencoders (LAEs).
Optimizing the standard L2 loss results in a decoder matrix that spans the principal subspace of the sample covariance of the data, but fails to identify the exact eigenvectors.
This downside originates from an invariance that cancels out in the global map.
Here, we prove that our loss function eliminates this issue, i.e. the decoder converges to the exact ordered unnormalized eigenvectors of the sample covariance matrix.
For this new loss, we establish that all local minima are global optima and also show that computing the new loss (and also its gradients) has the same order of complexity as the classical loss.
We report numerical results on both synthetic simulations, and a real-data PCA experiment on MNIST (i.e., a 60,000 x784 matrix), demonstrating our approach to be practically applicable and rectify previous LAEs' downsides.

assistant: text: A new loss function for PCA with linear autoencoders that provably yields ordered exact eigenvectors
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Hallucinations in neural machine translation

Paper content:
Neural machine translation (NMT) systems have reached state of the art performance in translating text and widely deployed.
Yet little is understood about how these systems function or break.
Here we show that NMT systems are susceptible to producing highly pathological translations that are completely untethered from the source material, which we term hallucinations.
Such pathological translations are problematic because they are are deeply disturbing of user trust and easy to find.
We describe a method t generate hallucinations and show that many common variations of the NMT architecture are susceptible to them.
We study a variety of approaches to reduce the frequency of hallucinations, including data augmentation, dynamical systems and regularization techniques and show that data augmentation significantly reduces hallucination frequency.
Finally, we analyze networks that produce hallucinations and show signatures of hallucinations in the attention matrix and in the stability measures of the decoder.

assistant: text: We introduce and analyze the phenomenon of "hallucinations" in NMT, or spurious translations unrelated to source text, and propose methods to reduce its frequency.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Generate Filters for Convolutional Neural Networks

Paper content:
Conventionally, convolutional neural networks (CNNs) process different images with the same set of filters.
However, the variations in images pose a challenge to this fashion.
In this paper, we propose to generate sample-specific filters for convolutional layers in the forward pass.
Since the filters are generated on-the-fly, the model becomes more flexible and can better fit the training data compared to traditional CNNs.
In order to obtain sample-specific features, we extract the intermediate feature maps from an autoencoder.
As filters are usually high dimensional, we propose to learn a set of coefficients instead of a set of filters.
These coefficients are used to linearly combine the base filters from a filter repository to generate the final filters for a CNN.
The proposed method is evaluated on MNIST, MTFL and CIFAR10 datasets.
Experiment results demonstrate that the classification accuracy of the baseline model can be improved by using the proposed filter generation method.

assistant: text: dynamically generate filters conditioned on the input image for CNNs in each forward pass
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Spoken Term Detection Automatically Adjusted for a Given Threshold

Paper content:
Spoken term detection (STD) is the task of determining whether and where a given word or phrase appears in a given segment of speech.
Algorithms for STD are often aimed at maximizing the gap between the scores of positive and negative examples.
As such they are focused on ensuring that utterances where the term appears are ranked higher than utterances where the term does not appear.
However, they do not determine a detection threshold between the two.
In this paper, we propose a new approach for setting an absolute detection threshold for all terms by introducing a new calibrated loss function.
The advantage of minimizing this loss function during training is that it aims at maximizing not only the relative ranking scores, but also adjusts the system to use a fixed threshold and thus enhances system robustness and maximizes the detection accuracy rates.
We use the new loss function in the structured prediction setting and extend the discriminative keyword spotting algorithm for learning the spoken term detector with a single threshold for all terms.
We further demonstrate the effectiveness of the new loss function by applying it on a deep neural Siamese network in a weakly supervised setting for template-based spoken term detection, again with a single fixed threshold.
Experiments with the TIMIT, WSJ and Switchboard corpora showed that our approach not only improved the accuracy rates when a fixed threshold was used but also obtained higher Area Under Curve (AUC).

assistant: text: Spoken Term Detection, using structured prediction and deep networks, implementing a new loss function that both maximizes AUC and ranks according to a predefined threshold.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Differentiable Cross-Entropy Method

Paper content:
We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant (DCEM) that enables us to differentiate the output of CEM with respect to the objective function's parameters.
In the machine learning setting this brings CEM inside of the end-to-end learning pipeline in cases this has otherwise been impossible.
We show applications in a synthetic energy-based structured prediction task and in non-convex continuous control.
In the control setting we show on the simulated cheetah and walker tasks that we can embed their optimal action sequences with DCEM and then use policy optimization to fine-tune components of the controller as a step towards combining model-based and model-free RL.

assistant: text: DCEM learns latent domains for optimization problems and helps bridge the gap between model-based and model-free RL --- we create a differentiable controller and fine-tune parts of it with PPO
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Momentary fluctuations in attention (perceptual accuracy) correlate with neural activity fluctuations in primate visual areas.
Yet, the link between such momentary neural fluctuations and attention state remains to be shown in the human brain.
We investigate this link using a real-time cognitive brain machine interface (cBMI) based on steady state visually evoked potentials (SSVEPs): occipital EEG potentials evoked by rhythmically flashing stimuli.
Tracking momentary fluctuations in SSVEP power, in real-time, we presented stimuli time-locked to when this power reached (predetermined) high or low thresholds.
We observed a significant increase in discrimination accuracy (d') when stimuli were triggered during high (versus low) SSVEP power epochs, at the location cued for attention.
Our results indicate a direct link between attention’s effects on perceptual accuracy and and neural gain in EEG-SSVEP power, in the human brain.
To identify efficient acquisition software, we compared the round-trip (closed-loop) delay for four 34 acquisition software: ActiView, Lab Streaming Layer, OpenVibe and Fieldtrip (Fig. 1B) .
We measured 35 round-trip delay by varying the EEG+event packet size, across different sampling frequencies, fit a 36 line to the data, and estimated the intercept, which is a measure of the overhead.
We observed that
Fieldtrip produced the least overhead of 10.98 ± 0.50 ms.
EEG data recording: Scalp EEG recordings were performed with 41 occipital electrodes out of 39 the total 128 electrodes.
The data was streamed in real-time using the Fieldtrip buffer at 128 Hz.
EEG 40 data was also stored at 4096 Hz for offline analyses.
Spectral analysis was performed using Chronux 41 2.12 toolbox (1) EEGLAB 13.6.5b (4) functions were used to generate the topographical plots.
Dense-array Studies (5)).
Finally, the EEG data was re-referenced to the average reference.
higher, across the population, for AI-high trials as compared to AI-low trials (p < 0.01).
We did not

assistant: text: With a cognitive brain-machine interface, we show a direct link between attentional effects on perceptual accuracy and neural gain in EEG-SSVEP power, in the human brain.