Regression Networks for Meta-Learning Few-Shot Classification

We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. In high dimensional embedding spaces the direction of data generally contains richer information than magnitude. Next to this, state-of-the-art few-shot metric methods that compare distances with aggregated class representations, have shown superior performance. Combining these two insights, we propose to meta-learn classification of embedded points by regressing the closest approximation in every class subspace while using the regression error as a distance metric. Similarly to recent approaches for few-shot learning, regression networks reflect a simple inductive bias that is beneficial in this limited-data regime and they achieve excellent results, especially when more aggregate class representations can be formed with multiple shots.Comment: 7th ICML Workshop on Automated Machine Learning (2020

[Re] Meta learning with differentiable closed-form solvers

In this work, we present a reproduction of the paper of Bertinetto et al. ”Meta- learning with differentiable closed-form solvers” as part of the ICLR 2019 Reproducibility Challenge. In successfully reproducing the most crucial part of the paper, we reach a performance that is comparable with or superior to the original paper on two benchmarks for several settings. We evaluate new baseline results, using a new dataset presented in the paper. Yet, we also provide multiple remarks and recommendations about reproducibility and comparability. After we brought our reproducibility work to the authorsʼ attention, they have updated the original paper on which this work is based and released code as well. Our contributions mainly consist in reproducing the most important results of their original paper, in giving insight in the reproducibility and in providing a first open-source implementation

Area and Power Efficient Ultra-Wideband Transmitter Based on Active Inductor

This paper presents the design of an impulse radio ultra-wideband (IR-UWB) transmitter for low-power, short-range, and high-data rate applications such as high density neural recording interfaces. The IR-UWB transmitter pulses are generated by modulating the output of a local oscillator. The large area requirement of the spiral inductor in a conventional on-chip LC tank is overcome by replacing it with an active inductor topology. The circuit has been fabricated in a UMC CMOS 180 nm technology, with a die area of 0.012 mm2. The temporal width of the output waveform is determined by a pulse generator based on logic gates. The measured pulse is compliant with Federal Communications Commission (FCC) power spectral density limits and within the frequency band of 3-6 GHz. For the minimum pulse duration of 1 ns, the energy consumption of the design is 20 pJ per bit, while transmitting at a 200 Mbps data rate with an amplitude of 130 mV

Profit Maximizing Logistic Regression Modeling for Credit Scoring

Multiple classification techniques have been employed for different business applications. In the particular case of credit scoring, a classifier which maximizes the total profit is preferable. The recently proposed expected maximum profit (EMP) measure for credit scoring allows to select the most profitable classifier. Taking the idea of the EMP one step further, it is desirable to integrate the measure into model construction, and thus obtain a profit maximizing model. Therefore, in this work we propose a method based on the ProfLogit classifier, which optimizes the coefficients of a logistic regression model using a genetic algorithm. The proposed implemented technique shows a significant improvement compared to regular maximum likelihood based logistic regression models on real-life data sets in terms of total profit, which is the ultimate goal for most businesses.</p

Multiphase Digitally Controlled Oscillator for Future 5G Phased Arrays in 90 nm CMOS

This paper reports a low noise Digitally Controlled Oscillator (DCO) with multiphase outputs, suitable for next generation phased arrays. The DCO core is implemented using an 8 stage Rotary Traveling Wave Oscillator (RTWO) topology. Simple design equations are presented and insight is given in the layout implementation. Designed in a 90 nm CMOS process, the prototype is tunable from 31.4 to 37 GHz (i.e. 16% tuning range). Drawing 45 mW from a 1.2V supply, the simulated phase noise is -127.3 dBc/Hz at 10 MHz offset from a 34 GHz carrier, resulting in a phase-noise FoM of -181.4 dBc/Hz. Digitally tuned slow wave transmission lines are used to achieve a fine tuning resolution of 1.8 MHz, resulting in a state-of-the-art tuning FoMDT of -187 dBc/Hz.status: publishe

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Recent advances in transfer learning and few-shot learning largely rely on annotated data related to the goal task during (pre-)training. However, collecting sufficiently similar and annotated data is often infeasible. Building on advances in self-supervised and few-shot learning, we propose to learn a metric embedding that clusters unlabeled samples and their augmentations closely together. This pre-trained embedding serves as a starting point for classification with limited labeled goal task data by summarizing class clusters and fine-tuning. Experiments show that our approach significantly outperforms state-of the-art unsupervised meta-learning approaches, and is on par with supervised performance. In a cross-domain setting, our approach is competitive with its classical fully supervised counterpart