nils-herrmann/clean_scirepeval huggingface dataset:
The Expressive Power of Gated Recurrent Units as a Continuous Dynamical System
Authors: Ian D. Jordan, Piotr Aleksander Sokol, Il Memming Park

Gated recurrent units (GRUs) were inspired by the common gated recurrent unit, long short-term memory (LSTM), as a means of capturing temporal structure with less complex memory unit architecture. Despite their incredible success in tasks such as natural and artificial language processing, speech, video, and polyphonic music, very little is understood about the specific dynamic features representable in a GRU network. As a result, it is difficult to know a priori how successful a GRU-RNN will perform on a given data set. In this paper, we develop a new theoretical framework to analyze one and two dimensional GRUs as a continuous dynamical system, and classify the dynamical features obtainable with such system.
We found rich repertoire that includes stable limit cycles over time (nonlinear oscillations), multi-stable state transitions with various topologies, and homoclinic orbits. In addition, we show that any finite dimensional GRU cannot precisely replicate the dynamics of a ring attractor, or more generally, any continuous attractor, and is limited to finitely many isolated fixed points in theory. These findings were then experimentally verified in two dimensions by means of time series prediction.
Composing Entropic Policies using Divergence Correction
Authors: Jonathan J Hunt, Andre Barreto, Timothy P Lillicrap, Nicolas Heess

Deep reinforcement learning (RL) algorithms have made great strides in recent years. An important remaining challenge is the ability to quickly transfer existing skills to novel tasks, and to combine existing skills with newly acquired ones. In domains where tasks are solved by composing skills this capacity holds the promise of dramatically reducing the data requirements of deep RL algorithms, and hence increasing their applicability. Recent work has studied ways of composing behaviors represented in the form of action-value functions. We analyze these methods to highlight their strengths and weaknesses, and point out situations where each of them is susceptible to poor performance. To perform this analysis we extend generalized policy improvement to the max-entropy framework and introduce a method for the practical implementation of successor features in continuous action spaces. Then we propose a novel approach which, in principle, recovers the optimal policy during transfer. This method works by explicitly learning the (discounted, future) divergence between policies. We study this approach in the  tabular case and propose a scalable variant that is applicable in multi-dimensional continuous action spaces.
We compare our approach with existing ones on a range of non-trivial continuous control problems with compositional structure, and demonstrate qualitatively better performance despite not requiring simultaneous observation of all task rewards.
Looking Back on Learned Experiences  For Class/task Incremental Learning
Authors: Mozhgan PourKeshavarzi, Guoying Zhao, Mohammad Sabokrou

Classical deep neural networks are limited in their ability to learn from emerging streams of training data. When trained sequentially on new or evolving tasks, their performance degrades sharply, making them inappropriate in real-world use cases. Existing methods tackle it by either storing old data samples or only updating a parameter set of deep neural networks, which, however, demands a large memory budget or spoils the flexibility of models to learn the incremented task distribution. In this paper, we shed light on an on-call transfer set to provide past experiences whenever a new task arises in the data stream. In particular, we propose a Cost-Free Incremental Learning (CF-IL) not only to replay past experiences the model has learned but also to perform this in a cost free manner. Towards this end, we introduced a memory recovery paradigm in which we query the network to synthesize past exemplars whenever a new task emerges. Thus, our method needs no extra memory for data buffering or network growing, besides calls the proposed memory recovery paradigm to provide past exemplars, named a transfer set in order to mitigate catastrophically forgetting the former tasks in the Incremental Learning (IL) setup. Moreover, in contrast with recently proposed methods, the suggested paradigm does not desire a parallel architecture since it only relies on the learner network. Compared to the state-of-the-art data techniques without buffering past data samples, CF-IL demonstrates significantly better performance on the well-known datasets whether a task oracle is available in test time (Task-IL) or not (Class-IL).
An information-theoretic framework for learning models of instance-independent label noise
Authors: Xia Huang, Kai Fong Ernest Chong

Given a dataset $\mathcal{D}$ with label noise, how do we learn its underlying noise model? If we assume that the label noise is instance-independent, then the noise model can be represented by a noise transition matrix $Q_{\mathcal{D}}$. Recent work has shown that even without further information about any instances with correct labels, or further assumptions on the distribution of the label noise, it is still possible to estimate $Q_{\mathcal{D}}$ while simultaneously learning a classifier from $\mathcal{D}$. However, this presupposes that a good estimate of $Q_{\mathcal{D}}$ requires an accurate classifier. In this paper, we show that high classification accuracy is actually not required for estimating $Q_{\mathcal{D}}$ well. We shall introduce an information-theoretic-based framework for estimating $Q_{\mathcal{D}}$ solely from $\mathcal{D}$ (without additional information or assumptions). At the heart of our framework is a discriminator that predicts whether an input dataset has maximum Shannon entropy, which shall be used on multiple new datasets $\hat{\mathcal{D}}$ synthesized from $\mathcal{D}$ via the insertion of additional label noise. We prove that our estimator for $Q_{\mathcal{D}}$ is statistically consistent, in terms of dataset size, and the number of intermediate datasets $\hat{\mathcal{D}}$ synthesized from $\mathcal{D}$. As a concrete realization of our framework, we shall incorporate local intrinsic dimensionality (LID) into the discriminator, and we show experimentally that with our LID-based discriminator, the estimation error for $Q_{\mathcal{D}}$ can be significantly reduced. We achieved average Kullback--Leibler loss reduction from $0.27$ to $0.17$ for $40\%$ anchor-like samples removal when evaluated on the CIFAR10 with symmetric noise. Although no clean subset of $\mathcal{D}$ is required for our framework to work, we show that our framework can also take advantage of clean data to improve upon existing estimation methods.
Language-driven Semantic Segmentation
Authors: Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, Rene Ranftl

We present LSeg, a novel model for language-driven semantic image segmentation. LSeg uses a text encoder to compute embeddings of descriptive input labels (e.g., ``grass'' or ``building'') together with a transformer-based image encoder that computes dense per-pixel embeddings of the input image. The image encoder is trained with a contrastive objective to align pixel embeddings to the text embedding of the corresponding semantic class. The text embeddings provide a flexible label representation in which semantically similar labels map to similar regions in the embedding space (e.g., ``cat'' and ``furry''). This allows LSeg to generalize to previously unseen categories at test time, without retraining or even requiring a single additional training sample. We demonstrate that our approach achieves highly competitive zero-shot performance compared to existing zero- and few-shot semantic segmentation methods, and even matches the accuracy of traditional segmentation algorithms when a fixed label set is provided. Code and demo are available at https://github.com/isl-org/lang-seg.
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics
Authors: Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Joshua B. Tenenbaum, Charles Sutton

Humans are capable of reasoning about physical phenomena by inferring laws of physics from a very limited set of observations. The inferred laws can potentially depend on unobserved properties, such as mass, texture, charge, etc. This sample-efficient physical reasoning is considered a core domain of human common-sense knowledge and hints at the existence of a physics engine in the head. In this paper, we propose a Bayesian symbolic framework for learning sample-efficient models of physical reasoning and prediction, which are of special interests in the field of intuitive physics. In our framework, the environment is represented by a top-down generative model with a collection of entities with some known and unknown properties as latent variables to capture uncertainty. The physics engine depends on physical laws which are modeled as interpretable symbolic expressions and are assumed to be functions of the latent properties of the entities interacting under simple Newtonian physics. As such, learning the laws is then reduced to symbolic regression and Bayesian inference methods are used to obtain the distribution of unobserved properties. These inference and regression steps are performed in an iterative manner following the expectation–maximization algorithm to infer the unknown properties and use them to learn the laws from a very small set of observations. We demonstrate that on three physics learning tasks that compared to the existing methods of learning physics, our proposed framework is more data-efficient, accurate and makes joint reasoning and learning possible.
Physics Informed Convex Artificial Neural Networks (PICANNs) for Optimal Transport based Density Estimation
Authors: Amanpreet Singh, Martin Bauer, Sarang Joshi

Optimal Mass Transport (OMT) is a well studied problem with a variety of applications in a  diverse set of fields ranging from Physics to Computer Vision and in particular Statistics and Data Science. Since the original formulation of Monge in 1781 significant theoretical progress been made on the existence, uniqueness and properties of the optimal transport maps. The actual numerical computation of the transport maps, particularly in high dimensions, remains a challenging problem. In the past decade several neural network based algorithms have been proposed to tackle this task. In this paper, building on recent developments of input convex neural networks and physics informed neural networks for solving PDE's, we propose a new Deep Learning approach to solve the continuous OMT problem. Our framework is based on Brenier's theorem, which reduces the continuous OMT problem to that of solving a non-linear PDE of Monge-Ampere type whose solution is a convex function. To demonstrate the accuracy of our framework we compare our method to several other deep learning based algorithms. We then focus on applications to the ubiquitous density estimation and generative modeling tasks in statistics and machine learning. Finally as an example we present how our framework can be incorporated with an autoencoder to estimate an effective probabilistic generative model.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Authors: Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, Eugene Belilovsky

In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes’ representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates.  Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks.
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
Authors: Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil Lawrence, Andreas Damianou

We propose a meta-learning approach that learns from multiple tasks in a transductive setting, by leveraging the unlabeled query set in addition to the  support set to generate a more powerful model for each task. To develop our framework, we revisit the empirical Bayes formulation for multi-task learning.   The evidence lower bound of the marginal log-likelihood of empirical Bayes decomposes as a sum of local KL divergences between the variational posterior and the true posterior on the query set of each task.
We derive a novel amortized variational inference that couples all the variational posteriors via a meta-model, which consists of a synthetic gradient   network and an initialization network. Each variational posterior is derived from synthetic gradient descent to approximate the true posterior on the query  set, although where we do not have access to the true gradient.
Our results on the Mini-ImageNet and CIFAR-FS benchmarks for episodic few-shot classification outperform previous state-of-the-art methods. Besides, we conduct two zero-shot learning experiments to further explore the potential of the synthetic gradient.
A Flexible Approach to Automated RNN Architecture Generation
Authors: Martin Schrimpf, Stephen Merity, James Bradbury, Richard Socher

The process of designing neural architectures requires expert knowledge and extensive trial and error.
While automated architecture search may simplify these requirements, the recurrent neural network (RNN) architectures generated by existing methods are limited in both flexibility and components.
We propose a domain-specific language (DSL) for use in automated architecture search which can produce novel RNNs of arbitrary depth and width.
The DSL is flexible enough to define standard architectures such as the Gated Recurrent Unit and Long Short Term Memory and allows the introduction of non-standard RNN components such as trigonometric curves and layer normalization.  Using two different candidate generation techniques, random search with a ranking function and reinforcement learning, 
we explore the novel architectures produced by the RNN DSL for language modeling and machine translation domains.
The resulting architectures do not follow human intuition yet perform well on their targeted tasks, suggesting the space of usable RNN architectures is far larger than previously assumed.
Generative Discovery of Relational Medical Entity Pairs
Authors: Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies. To promote these benefits, it is desired to effectively expand the scale of high-quality yet novel relational medical entity pairs that embody rich medical knowledge in a structured form. To fulfill this goal, we introduce a generative model called Conditional Relationship Variational Autoencoder (CRVAE), which can discover meaningful and novel relational medical entity pairs without the requirement of additional external knowledge. Rather than discriminatively identifying the relationship between two given medical entities in a free-text corpus, we directly model and understand medical relationships from diversely expressed medical entity pairs. The proposed model introduces the generative modeling capacity of variational autoencoder to entity pairs, and has the ability to discover new relational medical entity pairs solely based on the existing entity pairs. Beside entity pairs, relationship-enhanced entity representations are obtained as another appealing benefit of the proposed method. Both quantitative and qualitative evaluations on real-world medical datasets demonstrate the effectiveness of the proposed method in generating relational medical entity pairs that are meaningful and novel.
Learning-based Support Estimation in Sublinear Time
Authors: Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner

We consider the  problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a random sample of its elements. The problem occurs in many applications, including biology, genomics, computer systems and linguistics. A line of research spanning the last decade resulted in algorithms that estimate the support up to $ \pm \varepsilon n$ from a sample of size $O(\log^2(1/\varepsilon) \cdot n/\log n)$, where $n$ is the data set size.  Unfortunately, this bound is known to be tight, limiting further improvements to the complexity of this problem. In this paper we consider estimation algorithms augmented with a machine-learning-based predictor that, given any element, returns an estimation of  its frequency.  We show that if the predictor is correct up to a constant approximation factor, then the sample complexity can be reduced significantly,  to
$$ \ \log (1/\varepsilon) \cdot n^{1-\Theta(1/\log(1/\varepsilon))}. 
We evaluate the proposed algorithms on a collection of data sets, using the neural-network based estimators from {Hsu et al, ICLR'19} as predictors. Our experiments  demonstrate substantial (up to 3x) improvements in the estimation accuracy compared to the state of the art algorithm.
Illiterate DALL-E Learns to Compose
Authors: Gautam Singh, Fei Deng, Sungjin Ahn

Although DALL-E has shown an impressive ability of composition-based systematic generalization in image generation, it requires the dataset of text-image pairs and the compositionality is provided by the text. In contrast, object-centric representation models like the Slot Attention model learn composable representations without the text prompt. However, unlike DALL-E, its ability to systematically generalize for zero-shot generation is significantly limited. In this paper, we propose a simple but novel slot-based autoencoding architecture, called SLATE, for combining the best of both worlds: learning object-centric representations that allow systematic generalization in zero-shot image generation without text. As such, this model can also be seen as an illiterate DALL-E model. Unlike the pixel-mixture decoders of existing object-centric representation models, we propose to use the Image GPT decoder conditioned on the slots for capturing complex interactions among the slots and pixels. In experiments, we show that this simple and easy-to-implement architecture not requiring a text prompt achieves significant improvement in in-distribution and out-of-distribution (zero-shot) image generation and qualitatively comparable or better slot-attention structure than the models based on mixture decoders.
Graph Edit Networks
Authors: Benjamin Paassen, Daniele Grattarola, Daniele Zambon, Cesare Alippi, Barbara Eva Hammer

While graph neural networks have made impressive progress in classification and regression, few approaches to date perform time series prediction on graphs, and those that do are mostly limited to edge changes. We suggest that graph edits are a more natural interface for graph-to-graph learning. In particular,  graph edits are general enough to describe any graph-to-graph change, not only edge changes; they are sparse, making them easier to understand for humans and more efficient computationally; and they are local, avoiding the need for pooling layers in graph neural networks. In this paper, we propose a novel output layer - the graph edit network - which takes node embeddings as input and generates a sequence of graph edits that transform the input graph to the output graph. We prove that a mapping between the node sets of two graphs is sufficient to construct training data for a graph edit network and that an optimal mapping yields edit scripts that are almost as short as the graph edit distance between the graphs. We further provide a proof-of-concept empirical evaluation on several graph dynamical systems, which are difficult to learn for baselines from the literature.
Weakly Supervised Disentanglement with Guarantees
Authors: Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole

Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns about the viability of learning disentangled representations in a purely unsupervised manner has spurred a shift toward the incorporation of weak supervision. However, there is currently no formalism that identifies when and how weak supervision will guarantee disentanglement. To address this issue, we provide a theoretical framework to assist in analyzing the disentanglement guarantees (or lack thereof) conferred by weak supervision when coupled with learning algorithms based on distribution matching. We empirically verify the guarantees and limitations of several weak supervision methods (restricted labeling, match-pairing, and rank-pairing), demonstrating the predictive power and usefulness of our theoretical framework.
LEARNING DISTRIBUTIONS GENERATED  BY SINGLE-LAYER RELU  NETWORKS  IN  THE PRESENCE  OF ARBITRARY OUTLIERS
Authors: Saikiran Bulusu, Geethu Joseph, M. Cenk Gursoy, Pramod Varshney

We consider a set of data samples such that a constant fraction of the samples are arbitrary outliers and the rest are the output samples of a single-layer neural network (NN) with rectified linear unit (ReLU) activation. The goal of this paper is to estimate the parameters (weight matrix and bias vector) of the NN assuming the bias vector to be non-negative. Our proposed method is a two-step algorithm. We first estimate the norms of the rows of the weight matrix and the bias vector using the gradient descent algorithm. Here, we also incorporate either the median or the trimmed mean based filters to mitigate the effect of the arbitrary outliers. Next, we estimate the angles between any two row vectors of the weight matrix. Combining the estimates of the norms and the angles, we obtain the final estimate of the weight matrix. Further, we prove that ${O}(\frac{1}{\epsilon p^4}\log\frac{d}{\delta})$ samples are sufficient for our algorithm to estimate the NN parameters within an error of $\epsilon$ with probability $1-\delta$ when the probability of a sample being uncorrupted is $p$ and the problem dimension is $d$. Our theoretical and simulation results provide insights on how the estimation of the NN parameters depends on the probability of a sample being uncorrupted, the number of samples, and the problem dimension.