nils-herrmann/clean_scirepeval huggingface dataset:
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time
Authors: Ferran Alet, Kenji Kawaguchi, Maria Bauza Villalonga, Nurullah Giray Kuru, Tomas Perez, Leslie Pack Kaelbling

From CNNs to attention mechanisms, encoding inductive biases into neural networks has been a fruitful source of improvement in machine learning. Auxiliary losses are a general way of encoding biases in order to help networks learn better representations by adding extra terms to the loss function. However, since they are minimized on the training data, they suffer from the same generalization gap as regular task losses. Moreover, by changing the loss function, the network is optimizing a different objective than the one we care about. In this work we solve both problems: first, we take inspiration from transductive learning and note that, after receiving an input but before making a prediction, we can fine-tune our models on any unsupervised objective. We call this process tailoring, because we customize the model to each input. Second, we formulate a nested optimization (similar to those in meta-learning) and train our models to perform well on the task loss after adapting to the tailoring loss. The advantages of tailoring and meta-tailoring are discussed theoretically and demonstrated empirically on several diverse examples: encoding inductive conservation laws from physics to improve predictions, improving local smoothness to increase robustness to adversarial examples, and using contrastive losses on the query image to improve generalization.
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization
Authors: Tolga Ergen, Arda Sahiner, Batu Ozturkler, John M. Pauly, Morteza Mardani, Mert Pilanci

Batch Normalization (BN) is a commonly used technique to accelerate and stabilize training of deep neural networks. Despite its empirical success, a full theoretical understanding of BN is yet to be developed. In this work, we analyze BN through the lens of convex optimization. We introduce an analytic framework based on convex duality to obtain exact convex representations of weight-decay regularized ReLU networks with BN, which can be trained in polynomial-time. Our analyses also show that optimal layer weights can be obtained as simple closed-form formulas in the high-dimensional and/or overparameterized regimes. Furthermore, we find that Gradient Descent provides an algorithmic bias effect on the standard non-convex BN network, and we design an approach to explicitly encode this implicit regularization into the convex objective. Experiments with CIFAR image classification highlight the effectiveness of this explicit regularization for mimicking and substantially improving the performance of standard BN networks.
Cluster-based Feature Importance Learning for Electronic Health Record Time-series
Authors: Henrique Aguiar, Mauro Santos, Peter Watkinson, Tingting Zhu

The recent availability of Electronic Health Records (EHR) has allowed for the development of algorithms predicting inpatient risk of deterioration and trajectory evolution. However, prediction of disease progression with EHR is challenging since these data are sparse, heterogeneous, multi-dimensional, and multi-modal time-series. As such, clustering is used to identify similar groups within the patient cohort to improve prediction. Current models 
have shown some success in obtaining cluster representation of patient trajectories, however, they i) fail to obtain clinical interpretability for each cluster, and ii) struggle to learn meaningful cluster numbers in the context of the imbalanced distribution of disease outcomes. We propose a supervised deep learning model to cluster EHR data based on the identification of clinically understandable phenotypes with regard to both outcome prediction and patient trajectory. We introduce novel loss functions to address the problems of class imbalance and cluster collapse, and furthermore propose a feature-time attention mechanism to identify cluster-based phenotype importance across time and feature dimensions. We tested our model in over 100,000 unique trajectories from hospitalised patients with Type-II respiratory failure to predict five different outcomes. Our model yielded added interpretability to cluster formation and outperformed benchmarks by at least 5% in mean AUROC.
A Simple Recurrent Unit with Reduced Tensor Product Representations
Authors: Shuai Tang, Paul Smolensky, Virginia R. de Sa

Widely used recurrent units, including Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU), perform well on natural language tasks, but their ability to learn structured representations is still questionable. Exploiting reduced Tensor Product Representations (TPRs) --- distributed representations of symbolic structure in which vector-embedded symbols are bound to vector-embedded structural positions --- we propose the TPRU, a simple recurrent unit that, at each time step, explicitly executes structural-role binding and unbinding operations to incorporate structural information into learning. The gradient analysis of our proposed TPRU is conducted to support our model design, and its performance on multiple datasets shows the effectiveness of it. Furthermore, observations on linguistically grounded study demonstrate the interpretability of our TPRU.
Robust anomaly detection and backdoor attack detection via differential privacy
Authors: Min Du, Ruoxi Jia, Dawn Song

Outlier detection and novelty detection are two important topics for anomaly detection. Suppose the majority of a dataset are drawn from a certain distribution, outlier detection and novelty detection both aim to detect data samples that do not fit the distribution. Outliers refer to data samples within this dataset, while novelties refer to new samples. In the meantime, backdoor poisoning attacks for machine learning models are achieved through injecting poisoning samples into the training dataset, which could be regarded as “outliers” that are intentionally added by attackers. Differential privacy has been proposed to avoid leaking any individual’s information, when aggregated analysis is performed on a given dataset. It is typically achieved by adding random noise, either directly to the input dataset, or to intermediate results of the aggregation mechanism. In this paper, we demonstrate that applying differential privacy could improve the utility of outlier detection and novelty detection, with an extension to detect poisoning samples in backdoor attacks. We first present a theoretical analysis on how differential privacy helps with the detection, and then conduct extensive experiments to validate the effectiveness of differential privacy in improving outlier detection, novelty detection, and backdoor attack detection.
Self-Supervised Learning of Appliance Usage
Authors: Chen-Yu Hsu, Abbas Zeitoun, Guang-He Lee, Dina Katabi, Tommi Jaakkola

Learning home appliance usage is important for understanding people's activities and optimizing energy consumption.  The problem is modeled as an event detection task, where the objective is to learn when a user turns an appliance on, and which appliance it is (microwave, hair dryer, etc.). Ideally, we would like to solve the problem in an unsupervised way so that the method can be applied to new homes and new appliances without any labels. To this end, we introduce a new deep learning model that takes input from two home sensors: 1) a smart electricity meter that outputs the total energy consumed by the home as a function of time, and 2) a motion sensor that outputs the locations of the residents over time.  The model learns the distribution of the residents' locations conditioned on the home energy signal. We show that this cross-modal prediction task allows us to detect when a particular appliance is used, and the location of the appliance in the home, all in a self-supervised manner, without any labeled data.
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies
Authors: Paul Pu Liang, Manzil Zaheer, Yuan Wang, Amr Ahmed

Learning continuous representations of discrete objects such as text, users, movies, and URLs lies at the heart of many applications including language and user modeling. When using discrete objects as input to neural networks, we often ignore the underlying structures (e.g., natural groupings and similarities) and embed the objects independently into individual vectors. As a result, existing methods do not scale to large vocabulary sizes. In this paper, we design a simple and efficient embedding algorithm that learns a small set of anchor embeddings and a sparse transformation matrix. We call our method Anchor & Transform (ANT) as the embeddings of discrete objects are a sparse linear combination of the anchors, weighted according to the transformation matrix. ANT is scalable, flexible, and end-to-end trainable. We further provide a statistical interpretation of our algorithm as a Bayesian nonparametric prior for embeddings that encourages sparsity and leverages natural groupings among objects. By deriving an approximate inference algorithm based on Small Variance Asymptotics, we obtain a natural extension that automatically learns the optimal number of anchors instead of having to tune it as a hyperparameter. On text classification, language modeling, and movie recommendation benchmarks, we show that ANT is particularly suitable for large vocabulary sizes and demonstrates stronger performance with fewer parameters (up to 40x compression) as compared to existing compression baselines.
Mixture Representation Learning with Coupled Autoencoders
Authors: Yeganeh Marghi, Rohan Gala, Uygar Sümbül

Latent representations help unravel complex phenomena. While continuous latent variables can be efficiently inferred, fitting mixed discrete-continuous models remains challenging despite recent progress, especially when the discrete factor dimensionality is large. A pressing application for such mixture representations is the analysis of single-cell omic datasets to understand neuronal diversity and its molecular underpinnings. Here, we propose an unsupervised variational framework using multiple interacting networks called cpl-mixVAE that significantly outperforms state-of-the-art in high-dimensional discrete settings. cpl-mixVAE introduces a consensus constraint on discrete factors of variability across the networks, which regularizes the mixture representations at the time of training. We justify the use of this framework with theoretical results and validate it with experiments on benchmark datasets. We demonstrate that our approach discovers interpretable discrete and continuous variables describing neuronal identity in two single-cell RNA sequencing datasets, each profiling over a hundred cortical neuron types.
NOVEL AND EFFECTIVE PARALLEL MIX-GENERATOR GENERATIVE ADVERSARIAL NETWORKS
Authors: Xia Xiao, Sanguthevar Rajasekaran

In this paper, we propose a mix-generator generative adversarial networks (PGAN) model that works in parallel by mixing multiple disjoint generators to approximate a complex real distribution. In our model, we propose an adjustment component that collects all the generated data points from the generators, learns the boundary between each pair of generators, and provides error to separate the support of each of the generated distributions. To overcome the instability in a multiplayer game, a shrinkage adjustment component method is introduced to gradually reduce the boundary between generators during the training procedure. To address the linearly growing training time problem in a multiple generators model, we propose a method to train the generators in parallel. This means that our work can be scaled up to large parallel computation frameworks. We present an efficient loss function for the discriminator, an effective adjustment component, and a suitable generator. We also show how to introduce the decay factor to stabilize the training procedure. We have performed extensive experiments on synthetic datasets, MNIST, and CIFAR-10. These experiments reveal that the error provided by the adjustment component could successfully separate the generated distributions and each of the generators can stably learn a part of the real distribution even if only a few modes are contained in the real distribution.
Multi-Task Learning by a Top-Down Control Network
Authors: Hila Levi, Shimon Ullman

As the range of tasks performed by a general vision system expands, executing multiple tasks accurately and efﬁciently in  a single network has become an important and still open problem. Recent computer vision approaches address this problem by branching networks, or by a channel-wise modulation of the network feature-maps with task speciﬁc vectors. We present a novel architecture that uses a dedicated top-down control network to modify the activation of all the units in the main recognition network in a manner that depends on the selected task, image content, and spatial location. We show the effectiveness of our scheme by achieving signiﬁcantly better results than alternative state-of-the-art approaches on four datasets. We further demonstrate our advantages in terms of task selectivity, scaling the number of tasks and interpretability. 
Code is supplied in the supplementary materials and will be publicly available.
Learning Aggregation Functions
Authors: Giovanni Pellegrini, Alessandro Tibo, Paolo Frasconi, Andrea Passerini, Manfred Jaeger

Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability. Typically, representations over sets are computed by using fixed aggregation functions such as sum or maximum. However, recent results showed that universal function representation by sum- (or max-) decomposition requires either highly discontinuous (and thus poorly learnable) mappings, or a latent dimension equal to the maximum number of elements in the set. To mitigate this problem, we introduce LAF (Learning Aggregation Functions), a learnable aggregator for sets of arbitrary cardinality. LAF can approximate several extensively used aggregators (such as average, sum, maximum)  as well as more complex functions (e.g. variance and skewness). We report experiments on semi-synthetic and real data showing that LAF  outperforms state-of-the-art sum- (max-) decomposition architectures such as DeepSets and library-based architectures like Principal Neighborhood Aggregation.
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
Authors: Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov

Many deep learning applications benefit from using large models with billions of parameters. These models can only be trained with specialized distributed training algorithms that require low-latency and high-bandwidth interconnect. As a result, large models are typically trained in dedicated GPU clusters that can be extremely costly to deploy and operate. In contrast, there are more affordable distributed training setups, such as using cheap "preemptible" instances or pooling together existing resources from multiple regions. However, both these setups come with unique challenges that make it impractical to train large models using conventional model parallelism. In this work, we carefully analyze these challenges and find configurations where training larger models becomes less communication-intensive. Based on these observations, we propose SWARM Parallelism (Stochastically Wired Adaptively Rebalanced Model Parallelism) — a model-parallel training algorithm designed for swarms of poorly connected, heterogeneous unreliable devices. SWARM creates temporary randomized pipelines between available nodes that are rebalanced in case of failure. To further reduce the network usage of our approach, we develop several compression-aware architecture modifications and evaluate their tradeoffs. Finally, we combine our insights to train a large Transformer language model with 1.1B shared parameters (approximately 13B before sharing) on a swarm of preemptible T4 GPUs with less than 400Mb/s network throughput.
Learning with convolution and pooling operations in kernel methods
Authors: Theodor Misiakiewicz, Song Mei

Recent empirical work has shown that hierarchical convolutional kernels inspired by convolutional neural networks (CNNs) significantly improve the performance of kernel methods in image classification tasks. A widely accepted explanation for the success of these architectures is that they encode hypothesis classes that are suitable for natural images. However, understanding the precise interplay between approximation and generalization in convolutional architectures remains a challenge. In this paper, we consider the stylized setting of covariates (image pixels) uniformly distributed on the hypercube, and fully characterize the RKHS of kernels composed of single layers of convolution, pooling, and downsampling operations. We then study the gain in sample efficiency of kernel methods using these kernels over standard inner-product kernels. In particular, we show that 1) the convolution layer breaks the curse of dimensionality by restricting the RKHS to `local' functions; 2) local pooling biases learning towards low-frequency functions, which are stable by small translations; 3) downsampling may modify the high-frequency eigenspaces but leaves the low-frequency part approximately unchanged. Notably, our results quantify how choosing an architecture adapted to the target function leads to a large improvement in the sample complexity.
Counterpoint by Convolution
Authors: Cheng-Zhi Anna Huang, Tim Cooijmans, Adam Roberts, Aaron Courville, Douglas Eck

Machine learning models of music typically break down the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. We explore the use of blocked Gibbs sampling as an analogue to the human approach, and introduce Coconet, a convolutional neural network in the NADE family of generative models. Despite ostensibly sampling from the same distribution as the NADE ancestral sampling procedure, we find that a blocked Gibbs approach significantly improves sample quality. We provide evidence that this is due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling. We demonstrate the versatility of our method on unconditioned polyphonic music generation.
Cutting Down Training Memory by Re-fowarding
Authors: Jianwei Feng, Dong Huang

Deep Neutral Networks(DNNs) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory as a hardware resource is always finite, which limits the image resolution, batch size, and learning rate that could be used for better DNN performance. In this paper, we propose a novel training approach, called Re-forwarding, that substantially reduces memory usage in training. Our approach automatically finds a subset of vertices in a DNN computation graph, and stores tensors only at these vertices during the first forward. During backward, extra local forwards (called the Re-forwarding process) are conducted to compute the missing tensors between the subset of vertices. The total memory cost becomes the sum of (1) the memory cost at the subset of vertices and (2) the maximum memory cost among local re-forwards. Re-forwarding trades training time overheads for memory and does not compromise any performance in testing. We propose theories and algorithms that achieve the optimal memory solutions for DNNs with either linear or arbitrary computation graphs. Experiments show that Re-forwarding cuts down up-to 80% of training memory on popular DNNs such as Alexnet, VGG, ResNet, Densenet and Inception net.
Graph Similarities and Dual Approach for Sequential Text-to-Image Retrieval
Authors: Keonwoo Kim, Sihyeon Jo, Seong-Woo Kim

Sequential text-to-image retrieval, a.k.a. Story-to-images task, requires semantic alignment with a given story and maintaining global coherence in drawn image sequence simultaneously. Most of the previous works have only focused on modeling how to follow the content of a given story faithfully. This kind of overfitting tendency hinders matching structural similarity between images, causing an inconsistency in global visual information such as backgrounds. To handle this imbalanced problem, we propose a novel image sequence retrieval framework that utilizes scene graph similarities of the images and a dual learning scheme. Scene graph describes high-level information of visual groundings and adjacency relations of the key entities in a visual scene. In our proposed retriever, the graph encoding head learns to maximize graph embedding similarities among sampled images, giving a strong signal that forces the retriever to also consider morphological relevance with previously sampled images. We set a video captioning as a dual learning task that reconstructs the input story from the sampled image sequence. This inverse mapping gives informative feedback for our proposed retrieval system to maintain global contextual information of a given story. We also suggest a new contextual sentence encoding architecture to embed a sentence in consideration of the surrounding context. Through extensive experiments, Our proposed framework shows better qualitative and quantitative performance with Visual Storytelling benchmark compared to conventional story-to-image models.
Learning Global Additive Explanations for Neural Nets Using Model Distillation
Authors: Sarah Tan, Rich Caruana, Giles Hooker, Paul Koch, Albert Gordo

Interpretability has largely focused on local explanations, i.e. explaining why a model made a particular prediction for a sample. These explanations are appealing due to their simplicity and local fidelity. However, they do not provide information about the general behavior of the model. We propose to leverage model distillation to learn global additive explanations that describe the relationship between input features and model predictions. These global explanations take the form of feature shapes, which are more expressive than feature attributions. Through careful experimentation, we show qualitatively and quantitatively that global additive explanations are able to describe model behavior and yield insights about models such as neural nets. A visualization of our approach applied to a neural net as it is trained is available at https://youtu.be/ErQYwNqzEdc