1,720,981 research outputs found
Local Propagation in Neural Network Learning by Architectural Constraints
Full text linkA crucial role for the success of the Artificial Neural Networks (ANN) processing scheme has been played by the feed-forward propagation of signals.
The input patterns undergo a series of stacked parametrized transformations, which foster deep feature extraction and an increasing representational power. Each artificial neural network layer aggregates information from its incoming connections, projects it to another space, and immediately propagates it to the next layer.
Since its introduction in the '80s, BackPropagation (BP) is considered to be the ``de facto'' algorithm for training neural nets. The weights associated to the connections between the network layers are updated due to the backward pass, that is a straightforward derivation of the chain rule for the computation of the derivatives in a composition of functions. This computation requires to store all the intermediate values of the process. Moreover, it implies the use of non-local information, since the activity of one neuron has the ability to affect all the subsequent units up to the last output layer.
However, learning in the human brain can be considered a continuous, life-long and gradual process in which neuron activations fire, leveraging local information, both in space, e.g neighboring neurons, and time, e.g. previous states.
Following this principle, this thesis is inspired by the ideas of decoupling the computational scheme, behind the standard processing of ANNs, in order to decompose its overall structure into local components. Such local parts are put into communication leveraging the unifying notion of ``constraint''. In particular, a set of additional variables are added to the learning problem, in order to store the information on the status of the constrained neural units. Therefore, it is possible to describe the computations performed by the network itself guiding the evolution of these auxiliary variables via constraints. This choice allows us to setup an optimization procedure that is ``local'', i.e., it does not require (1) to query the whole network, (2) to accomplish the diffusion of the information, or (3) to bufferize data streamed over time in order to be able to compute gradients. The thesis investigates three different learning settings that are instances of the aforementioned scheme: (1) constraints among layers in feed-forward neural networks, (2) constraints among the states of neighboring nodes in Graph Neural Networks, and (3) constraints among predictions over time
Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks
Full text linkAmongst a variety of approaches aimed at making the learning procedure of neural networks more effective, the scientifc community developed strategies to order the examples
according to their estimated complexity, to distil knowledge
from larger networks, or to exploit the principles behind adversarial machine learning. A different idea has been recently
proposed, named Friendly Training, which consists in altering the input data by adding an automatically estimated perturbation, with the goal of facilitating the learning process
of a neural classifer. The transformation progressively fadesout as long as training proceeds, until it completely vanishes.
In this work we revisit and extend this idea, introducing a
radically different and novel approach inspired by the effectiveness of neural generators in the context of Adversarial
Machine Learning. We propose an auxiliary multi-layer network that is responsible of altering the input data to make
them easier to be handled by the classifer at the current stage
of the training procedure. The auxiliary network is trained
jointly with the neural classifer, thus intrinsically increasing
the “depth” of the classifer, and it is expected to spot general regularities in the data alteration process. The effect of
the auxiliary network is progressively reduced up to the end
of training, when it is fully dropped and the classifer is deployed for applications. We refer to this approach as Neural Friendly Training. An extended experimental procedure
involving several datasets and different neural architectures
shows that Neural Friendly Training overcomes the originally
proposed Friendly Training technique, improving the generalization of the classifer, especially in the case of noisy data
Continual learning of conjugated visual representations through higher-order motion flows
Full text linkLearning with neural networks from a continuous stream of visual information presents several challenges due to the non-i.i.d. nature of the data. However, it also offers novel opportunities to develop representations that are consistent with the information flow. In this paper we investigate the case of unsupervised continual learning of pixel-wise features subject to multiple motion-induced constraints, therefore named motion-conjugated feature representations. Differently from existing approaches, motion is not a given signal (either ground-truth or estimated by external modules), but is the outcome of a progressive and autonomous learning process, occurring at various levels of the feature hierarchy. Multiple motion flows are estimated with neural networks and characterized by different levels of abstractions, spanning from traditional optical flow to other latent signals originating from higher-level features, hence called higher-order motions. Continuously learning to develop consistent multi-order flows and representations is prone to trivial solutions, which we counteract by introducing a self-supervised contrastive loss, spatially-aware and based on flow-induced similarity. We assess our model on photorealistic synthetic streams and real-world videos, comparing to pre-trained state-of-the art feature extractors (also based on Transformers) and to recent unsupervised learning models, significantly outperforming these alternatives
Continual Learning with Pretrained Backbones by Tuning in the Input Space
No full textThe intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. This issue is critical in practical supervised learning settings, such as the ones in which a pre-trained model computes projections toward a latent space where different task predictors are sequentially learned over time. As a matter of fact, incrementally fine-tuning the whole model to better adapt to new tasks usually results in catastrophic forgetting, with decreasing performance over the past experiences and losing valuable knowledge from the pretraining stage. In this paper, we propose a novel strategy to make the fine-tuning procedure more effective, by avoiding to update the pre-trained part of the network and learning not only the usual classification head, but also a set of newly-introduced learnable parameters that are responsible for transforming the input data. This process allows the network to effectively leverage the pre-training knowledge and find a good trade-off between plasticity and stability with modest computational efforts, thus especially suitable for on-the-edge settings. Our experiments on four image classification problems in a continual learning setting confirm the quality of the proposed approach when compared to several fine-tuning procedures and to popular continual learning methods
Continual Neural Computation
No full textContinuously processing a stream of not-i.i.d. data by neural models with the goal of progressively learning new skills is largely known to introduce significant challenges, frequently leading to catastrophic forgetting. In this paper we tackle this problem focusing on the low-level aspects of the neural computation model, differently from the most common existing approaches. We propose a novel neuron model, referred to as Continual Neural Unit (CNU), which does not only compute a response to an input pattern, but also diversifies computations to preserve what was previously learned, while being plastic enough to adapt to new knowledge. The values attached to weights are the outcome of a computational process which depends on the neuron input, implemented by a key-value map to select and blend multiple sets of learnable memory units. This computational mechanism implements a natural, learnable form of soft parameter isolation, virtually defining multiple computational paths within each neural unit. We show that such a computational scheme is related to the ones of popular models which perform computations relying on a set of samples stored in a memory buffer, including Kernel Machines and Transformers. Experiments in class-and-domain incremental streams processed in online and single-pass manner show how CNUs can mitigate forgetting without any replays or more informed learning criteria, while keeping competitive or better performance with respect to continual learning methods that explicitly store and replay data
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Video surveillance of highway traffic events by deep learning architectures
No full textIn this paper we describe a video surveillance system able to detect traffic events in videos acquired by fixed videocameras on highways. The events of interest consist in a specific sequence of situations that occur in the video, as for instance a vehicle stopping on the emergency lane. Hence, the detection of these events requires to analyze a temporal sequence in the video stream. We compare different approaches that exploit architectures based on Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). A first approach extracts vectors of features, mostly related to motion, from each video frame and exploits a RNN fed with the resulting sequence of vectors. The other approaches are based directly on the sequence of frames, that are eventually enriched with pixel-wise motion information. The obtained stream is processed by an architecture that stacks a CNN and a RNN, and we also investigate a transfer-learning-based model. The results are very promising and the best architecture will be tested online in real operative conditions
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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