Reviewerly/exHarmony huggingface dataset:
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family

State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models. However, widespread use is constrained by device hardware limitations, resulting in a substantial performance gap between state-of-the-art models and those that can be effectively deployed on small devices. While Knowledge Distillation (KD) theoretically enables small student models to emulate larger teacher models, in practice selecting a good student architecture requires considerable human expertise. Neural Architecture Search (NAS) appears as a natural solution to this problem but most approaches can be inefficient, as most of the computation is spent comparing architectures sampled from the same distribution, with negligible differences in performance. In this paper, we propose to instead search for a family of student architectures sharing the property of being good at learning from a given teacher. Our approach AutoKD, powered by Bayesian Optimization, explores a flexible graph-based search space, enabling us to automatically learn the optimal student architecture distribution and KD parameters, while being 20x more sample efficient compared to existing state-of-the-art. We evaluate our method on 3 datasets; on large images specifically, we reach the teacher performance while using 3x less memory and 10x less parameters. Finally, while AutoKD uses the traditional KD loss, it outperforms more advanced KD variants using hand-designed students.
Identifying the Units of Measurement in Tabular Data

We consider the problem of identifying the units of measurement in a data column that contains both numeric values and unit symbols in each row, e.g., "5.2 l", "7 pints". In this case we seek to identify the dimension of the column (e.g. volume) and relate the unit symbols to valid units (e.g. litre, pint) obtained from a knowledge graph. Below we present PUC, a Probabilistic Unit Canonicalizer that can accurately identify the units of measurement, extract semantic descriptions of quantitative data columns and canonicalize their entries. We present the first messy real-world tabular datasets annotated for units of measurement, which can enable and accelerate the research in this area. Our experiments on these datasets show that PUC achieves better results than existing solutions.
Some topics in English newsmagazines in 2021, with special reference to mineral resources, historical building stones, and alternative interpretation of the Anthropocene

Information collected from geological newsmagazines in 2021 is reviewed. First topic is mineral resources such as mineral commodity summary in USA, resource potential of the East Siberia, and mineralogy of Ettringite and Nepheline. Second topic is the historical building stones, that is, review of IGCP-637, a bluish granite in Spain, and the Firenze building stone. Third is alternative interpretation of the Anthropocene, considering one of the geological epochs or not.
Missing Data Infill with Automunge

Missing data is a fundamental obstacle in the practice of data science. This paper surveys a few conventions for imputation as available in the Automunge open source python library platform for tabular data preprocessing, including "ML infill" in which auto ML models are trained for target features from partitioned extracts of a training set. A series of validation experiments were performed to benchmark imputation scenarios towards downstream model performance, in which it was found for the given benchmark sets that in many cases ML infill outperformed for both numeric and categoric target features, and was otherwise at minimum within noise distributions of the other imputation scenarios. Evidence also suggested supplementing ML infill with the addition of support columns with boolean integer markers signaling presence of infill was usually beneficial to downstream model performance. We consider these results sufficient to recommend defaulting to ML infill for tabular learning, and further recommend supplementing imputations with support columns signaling presence of infill, each as can be prepared with push-button operation in the Automunge library. Our contributions include an auto ML derived missing data imputation library for tabular learning in the python ecosystem, fully integrated into a preprocessing platform with an extensive library of feature transformations, with a novel production friendly implementation that bases imputation models on a designated train set for consistent basis towards additional data.
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions

We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these objects nor real images depicting them, only their CAD models. It relies on a small set of training objects to learn local object representations, which allow us to locally match the input image to a set of "templates", rendered images of the CAD models for the new objects. In contrast with the state-of-the-art methods, the new objects on which our method is applied can be very different from the training objects. As a result, we are the first to show generalization without retraining on the LINEMOD and Occlusion-LINEMOD datasets. Our analysis of the failure modes of previous template-based approaches further confirms the benefits of local features for template matching. We outperform the state-of-the-art template matching methods on the LINEMOD, Occlusion-LINEMOD and T-LESS datasets. Our source code and data are publicly available at https://github.com/nv-nguyen/template-pose
Revisiting Model-based Value Expansion

Model-based value expansion methods promise to improve the quality of value function targets and, thereby, the effectiveness of value function learning. However, to date, these methods are being outperformed by Dyna-style algorithms with conceptually simpler 1-step value function targets. This shows that in practice, the theoretical justification of value expansion does not seem to hold. We provide a thorough empirical study to shed light on the causes of failure of value expansion methods in practice which is believed to be the compounding model error. By leveraging GPU based physics simulators, we are able to efficiently use the true dynamics for analysis inside the model-based reinforcement learning loop. Performing extensive comparisons between true and learned dynamics sheds light into this black box. This paper provides a better understanding of the actual problems in value expansion. We provide future directions of research by empirically testing the maximum theoretical performance of current approaches.
Conformal testing: binary case with Markov alternatives

We continue study of conformal testing in binary model situations. In this note we consider Markov alternatives to the null hypothesis of exchangeability. We propose two new classes of conformal test martingales; one class is statistically efficient in our experiments, and the other class partially sacrifices statistical efficiency to gain computational efficiency.
Zero-Shot Sketch Based Image Retrieval using Graph Transformer

The performance of a zero-shot sketch-based image retrieval (ZS-SBIR) task is primarily affected by two challenges. The substantial domain gap between image and sketch features needs to be bridged, while at the same time the side information has to be chosen tactfully. Existing literature has shown that varying the semantic side information greatly affects the performance of ZS-SBIR. To this end, we propose a novel graph transformer based zero-shot sketch-based image retrieval (GTZSR) framework for solving ZS-SBIR tasks which uses a novel graph transformer to preserve the topology of the classes in the semantic space and propagates the context-graph of the classes within the embedding features of the visual space. To bridge the domain gap between the visual features, we propose minimizing the Wasserstein distance between images and sketches in a learned domain-shared space. We also propose a novel compatibility loss that further aligns the two visual domains by bridging the domain gap of one class with respect to the domain gap of all other classes in the training set. Experimental results obtained on the extended Sketchy, TU-Berlin, and QuickDraw datasets exhibit sharp improvements over the existing state-of-the-art methods in both ZS-SBIR and generalized ZS-SBIR.
Quantum circuit compilation and hybrid computation using Pauli-based computation

Pauli-based computation (PBC) is driven by a sequence of adaptively chosen, non-destructive measurements of Pauli observables. Any quantum circuit written in terms of the Clifford+$T$ gate set and having $t$ $T$ gates can be compiled into a PBC on $t$ qubits. Here we propose practical ways of implementing PBC as adaptive quantum circuits and provide code to do the required classical side-processing. Our schemes reduce the number of quantum gates to $O(t^2)$ (from a previous $O(t^3 / \log t)$ scaling) and space/time trade-offs are discussed which lead to a reduction of the depth from $O(t \log t)$ to $O(t)$ within our schemes, at the cost of $t$ additional auxiliary qubits. We compile examples of random and hidden-shift quantum circuits into adaptive PBC circuits. We also simulate hybrid quantum computation, where a classical computer effectively extends the working memory of a small quantum computer by $k$ virtual qubits, at a cost exponential in $k$. Our results demonstrate the practical advantage of PBC techniques for circuit compilation and hybrid computation.
Metric Analysis

In this chapter we will look at how to check the generalisation properties of trained neural networks. We will look especially at how to recognise overfitting and problems with data coming from different distributions.
Accounting for Gaussian Process Imprecision in Bayesian Optimization

Bayesian optimization (BO) with Gaussian processes (GP) as surrogate models is widely used to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we propose Prior-mean-RObust Bayesian Optimization (PROBO) that outperforms classical BO on specific problems. First, we study the effect of the Gaussian processes' prior specifications on classical BO's convergence. We find the prior's mean parameters to have the highest influence on convergence among all prior components. In response to this result, we introduce PROBO as a generalization of BO that aims at rendering the method more robust towards prior mean parameter misspecification. This is achieved by explicitly accounting for GP imprecision via a prior near-ignorance model. At the heart of this is a novel acquisition function, the generalized lower confidence bound (GLCB). We test our approach against classical BO on a real-world problem from material science and observe PROBO to converge faster. Further experiments on multimodal and wiggly target functions confirm the superiority of our method.
The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methods

In this work, we consider the performance of using a quantum algorithm to predict a result for a binary classification problem if a machine learning model is an ensemble from any simple classifiers. Such an approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let $N$ be a number of classifiers from an ensemble model and $O(T)$ be the running time of prediction on one classifier. In classical case, an ensemble model gets answers from each classifier and "averages" the result. The running time in classical case is $O\left( N \cdot T \right)$. We propose an algorithm which works in $O\left(\sqrt{N} \cdot T\right)$.
A full circuit-based quantum algorithm for excited-states in quantum chemistry

Utilizing quantum computer to investigate quantum chemistry is an important research field nowadays. In addition to the ground-state problems that have been widely studied, the determination of excited-states plays a crucial role in the prediction and modeling of chemical reactions and other physical processes. Here, we propose a non-variational full circuit-based quantum algorithm for obtaining the excited-state spectrum of a quantum chemistry Hamiltonian. Compared with previous classical-quantum hybrid variational algorithms, our method eliminates the classical optimization process, reduces the resource cost caused by the interaction between different systems, and achieves faster convergence rate and stronger robustness against noise without barren plateau. The parameter updating for determining the next energy-level is naturally dependent on the energy measurement outputs of the previous energy-level and can be realized by only modifying the state preparation process of ancillary system, introducing little additional resource overhead. Numerical simulations of the algorithm with hydrogen, LiH, H2O and NH3 molecules are presented. Furthermore, we offer an experimental demonstration of the algorithm on a superconducting quantum computing platform, and the results show a good agreement with theoretical expectations. The algorithm can be widely applied to various Hamiltonian spectrum determination problems on the fault-tolerant quantum computers.
StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2

Videos show continuous events, yet most $-$ if not all $-$ video synthesis frameworks treat them discretely in time. In this work, we think of videos of what they should be $-$ time-continuous signals, and extend the paradigm of neural representations to build a continuous-time video generator. For this, we first design continuous motion representations through the lens of positional embeddings. Then, we explore the question of training on very sparse videos and demonstrate that a good generator can be learned by using as few as 2 frames per clip. After that, we rethink the traditional image + video discriminators pair and design a holistic discriminator that aggregates temporal information by simply concatenating frames' features. This decreases the training cost and provides richer learning signal to the generator, making it possible to train directly on 1024$^2$ videos for the first time. We build our model on top of StyleGAN2 and it is just ${\approx}5\%$ more expensive to train at the same resolution while achieving almost the same image quality. Moreover, our latent space features similar properties, enabling spatial manipulations that our method can propagate in time. We can generate arbitrarily long videos at arbitrary high frame rate, while prior work struggles to generate even 64 frames at a fixed rate. Our model is tested on four modern 256$^2$ and one 1024$^2$-resolution video synthesis benchmarks. In terms of sheer metrics, it performs on average ${\approx}30\%$ better than the closest runner-up. Project website: https://universome.github.io.
ProtagonistTagger -- a Tool for Entity Linkage of Persons in Texts from Various Languages and Domains

Named entities recognition (NER) and disambiguation (NED) can add semantic context to the recognized named entities in texts. Named entity linkage in texts, regardless of a domain, provides links between the entities mentioned in unstructured texts and individual instances of real-world objects. In this poster, we present a tool - protagonistTagger - for person NER and NED in texts. The tool was tested on texts extracted from classic English novels and Polish Internet news. The tool's performance (both precision and recall) fluctuates between 78% and even 88%.
Signing the Supermask: Keep, Hide, Invert

The exponential growth in numbers of parameters of neural networks over the past years has been accompanied by an increase in performance across several fields. However, due to their sheer size, the networks not only became difficult to interpret but also problematic to train and use in real-world applications, since hardware requirements increased accordingly. Tackling both issues, we present a novel approach that either drops a neural network's initial weights or inverts their respective sign. Put simply, a network is trained by weight selection and inversion without changing their absolute values. Our contribution extends previous work on masking by additionally sign-inverting the initial weights and follows the findings of the Lottery Ticket Hypothesis. Through this extension and adaptations of initialization methods, we achieve a pruning rate of up to 99%, while still matching or exceeding the performance of various baseline and previous models. Our approach has two main advantages. First, and most notable, signed Supermask models drastically simplify a model's structure, while still performing well on given tasks. Second, by reducing the neural network to its very foundation, we gain insights into which weights matter for performance. The code is available on GitHub.
Guiding Neural Story Generation with Reader Models

Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. However, it is challenging to maintain coherence and stay on-topic toward a specific ending when generating narratives with neural language models. In this paper, we introduce Story generation with Reader Models (StoRM), a framework in which a reader model is used to reason about the story should progress. A reader model infers what a human reader believes about the concepts, entities, and relations about the fictional story world. We show how an explicit reader model represented as a knowledge graph affords story coherence and provides controllability in the form of achieving a given story world state goal. Experiments show that our model produces significantly more coherent and on-topic stories, outperforming baselines in dimensions including plot plausibility and staying on topic.
Stochastic Extragradient: General Analysis and Improved Rates

The Stochastic Extragradient (SEG) method is one of the most popular algorithms for solving min-max optimization and variational inequalities problems (VIP) appearing in various machine learning tasks. However, several important questions regarding the convergence properties of SEG are still open, including the sampling of stochastic gradients, mini-batching, convergence guarantees for the monotone finite-sum variational inequalities with possibly non-monotone terms, and others. To address these questions, in this paper, we develop a novel theoretical framework that allows us to analyze several variants of SEG in a unified manner. Besides standard setups, like Same-Sample SEG under Lipschitzness and monotonicity or Independent-Samples SEG under uniformly bounded variance, our approach allows us to analyze variants of SEG that were never explicitly considered in the literature before. Notably, we analyze SEG with arbitrary sampling which includes importance sampling and various mini-batching strategies as special cases. Our rates for the new variants of SEG outperform the current state-of-the-art convergence guarantees and rely on less restrictive assumptions.
Prediction Intervals for Simulation Metamodeling

Simulation metamodeling refers to the construction of lower-fidelity models to represent input-output relations using few simulation runs. Stochastic kriging, which is based on Gaussian process, is a versatile and common technique for such a task. However, this approach relies on specific model assumptions and could encounter scalability challenges. In this paper, we study an alternative metamodeling approach using prediction intervals to capture the uncertainty of simulation outputs. We cast the metamodeling task as an empirical constrained optimization framework to train prediction intervals that attain accurate prediction coverage and narrow width. We specifically use neural network to represent these intervals and discuss procedures to approximately solve this optimization problem. We also present an adaptation of conformal prediction tools as another approach to construct prediction intervals for metamodeling. Lastly, we present a validation machinery and show how our method can enjoy a distribution-free finite-sample guarantee on the prediction performance. We demonstrate and compare our proposed approaches with existing methods including stochastic kriging through numerical examples.
Philosophy’ Contribution to Human Capital Development of Researchers: Current State and Prospective Agenda

The article presents the results of the study of the role of philosophy in the training of scientific personnel. It is shown that adaptation to the challenges associated with changes in the do Russian education system and global educational trends is one of the main tasks modern Russian philosophy’s facing. The participation of philosophy in the preparation of future scientists has a special place among the problems arising in that connection. The problem originates from the ambiguous relations between philosophy and, first of all, natural sciences. Although in the Soviet system of higher education philosophy was one of the leading educational disciplines, its function with respect to science was to introduce disciplinary scientists to the only true – truly scientific – worldview; in other words, it was ideological and dogmatic in nature. This reinforced the positivism’ view on philosophy that was already formed in the scientific community in the second half of the nineteenth and early twentieth century. According to this philosophy is useless or harmful for science. It has been shown that the post-soviet period has required revision of the concept of philosophy as a scientific and university discipline, which has not been fully implemented. The paper outlines the main points that may serve as the foundation for this kind of concept, namely mutual cognitive goals, philosophy&amp;apos;s solution of cognitive tasks assumed by the existence of science, development of cognitive skills necessary for scientists, and initiation of knowledge that forms the ground for the development of non-cognitive skills within the framework of the philosophical studies. Finally, it is shown that the professional philosophical community can influence the level of philosophical education of disciplinary scientists and their perception of philosophy as part of the scientific system within the existing basic and postgraduate training courses, even in the absence of an increase in lecture and seminar courses, which is, however, also worth pursuing.
Towards Optimal Strategies for Training Self-Driving Perception Models  in Simulation

Autonomous driving relies on a huge volume of real-world data to be labeled to high precision. Alternative solutions seek to exploit driving simulators that can generate large amounts of labeled data with a plethora of content variations. However, the domain gap between the synthetic and real data remains, raising the following important question: What are the best ways to utilize a self-driving simulator for perception tasks? In this work, we build on top of recent advances in domain-adaptation theory, and from this perspective, propose ways to minimize the reality gap. We primarily focus on the use of labels in the synthetic domain alone. Our approach introduces both a principled way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. Our method is easy to implement in practice as it is agnostic of the network architecture and the choice of the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data (cameras, lidar) using an open-source simulator (CARLA), and evaluate the entire framework on a real-world dataset (nuScenes). Last but not least, we show what types of variations (e.g. weather conditions, number of assets, map design, and color diversity) matter to perception networks when trained with driving simulators, and which ones can be compensated for with our domain adaptation technique.