95 research outputs found
SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired Continual Learning
Deep neural networks (DNNs) struggle to learn in dynamic environments since
they rely on fixed datasets or stationary environments. Continual learning (CL)
aims to address this limitation and enable DNNs to accumulate knowledge
incrementally, similar to human learning. Inspired by how our brain
consolidates memories, a powerful strategy in CL is replay, which involves
training the DNN on a mixture of new and all seen classes. However, existing
replay methods overlook two crucial aspects of biological replay: 1) the brain
replays processed neural patterns instead of raw input, and 2) it prioritizes
the replay of recently learned information rather than revisiting all past
experiences. To address these differences, we propose SHARP, an efficient
neuro-inspired CL method that leverages sparse dynamic connectivity and
activation replay. Unlike other activation replay methods, which assume layers
not subjected to replay have been pretrained and fixed, SHARP can continually
update all layers. Also, SHARP is unique in that it only needs to replay few
recently seen classes instead of all past classes. Our experiments on five
datasets demonstrate that SHARP outperforms state-of-the-art replay methods in
class incremental learning. Furthermore, we showcase SHARP's flexibility in a
novel CL scenario where the boundaries between learning episodes are blurry.
The SHARP code is available at
\url{https://github.com/BurakGurbuz97/SHARP-Continual-Learning}
Neural Sculpting: Uncovering hierarchically modular task structure through pruning and network analysis
Natural target functions and tasks typically exhibit hierarchical modularity
- they can be broken down into simpler sub-functions that are organized in a
hierarchy. Such sub-functions have two important features: they have a distinct
set of inputs (input-separability) and they are reused as inputs higher in the
hierarchy (reusability). Previous studies have established that hierarchically
modular neural networks, which are inherently sparse, offer benefits such as
learning efficiency, generalization, multi-task learning, and transferability.
However, identifying the underlying sub-functions and their hierarchical
structure for a given task can be challenging. The high-level question in this
work is: if we learn a task using a sufficiently deep neural network, how can
we uncover the underlying hierarchy of sub-functions in that task? As a
starting point, we examine the domain of Boolean functions, where it is easier
to determine whether a task is hierarchically modular. We propose an approach
based on iterative unit and edge pruning (during training), combined with
network analysis for module detection and hierarchy inference. Finally, we
demonstrate that this method can uncover the hierarchical modularity of a wide
range of Boolean functions and two vision tasks based on the MNIST digits
dataset
Detecting network performance anomalies with contextual anomaly detection
Network performance anomalies can be defined as abnormal and significant variations in a network's traffic levels. Being able to detect anomalies is critical for both network operators and end users. However, the accurate detection without raising false alarms can become a challenging task when there is high variance in the traffic. To address this problem, we present in this paper a novel methodology for detecting performance anomalies based on contextual information. The proposed method is compared with the state of the art and is evaluated with high accuracy on both synthetic and real network traffic.Peer ReviewedPostprint (author's final draft
Can User-Level Probing Detect and Diagnose Common Home-WLAN Pathologies?
Common WLAN pathologies include low signal-to-noise ratio, congestion, hidden
terminals or interference from non-802.11 devices and phenomena. Prior work has
focused on the detection and diagnosis of such problems using layer-2
information from 802.11 devices and special-purpose access points and monitors,
which may not be generally available. Here, we investigate a userlevel
approach: is it possible to detect and diagnose 802.11 pathologies with
strictly user-level active probing, without any cooperation from, and without
any visibility in, layer-2 devices? In this paper, we present preliminary but
promising results indicating that such diagnostics are feasible
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