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Synthetic data enters its Cubist phase

Quants are using the theory of rough paths to distil the essence of financial datasets ....

City Of , United Kingdom , Imanol Perez , Terry Lyons , Pablo Picasso , Hans Buehler , James Morrill , Ben Wood , Blanka Horvath , Gertrude Stein , King College London , University Of Oxford , Jp Morgan , College London , Time Series Analysis , Machine Learning , Deep Learning , Synthetic Data , Deep Hedging , Sur Take , Cutting Edge , நகரம் ஆஃப் , ஒன்றுபட்டது கிஂக்டம் , டெர்ரி லயன்ஸ் , பப்லொ பிகாஸொ , ஹான்ஸ் பூஎளீர் ,

Forecasting developing Asian economies during normal times and large external shocks: Approaches and challenges | en

Predicting future economic trends appropriately is essential to economic policy making. Currently, the DSGE model approach is a benchmark economic forecasting technique widely employed. However, large external shocks, such as large-scale natural disasters and COVID-19, challenge current approaches to economic forecasting. Multiple approaches will be needed in this situation, including reduced-form model and indicator-based approaches. This paper discusses different forecasting approaches, by comparing forecasts during normal times and crisis periods. The Medium-term Projection Framework (MPF), used in the Economic Outlook for Southeast Asia, China and India series, receives particular attention. The paper also examines challenges unique to developing Asia and large external shock periods. The measurement of potential output, difficulties in modelling the credit channel, and the incorporation of Big Data pose challenges regarding developing Asian countries, and large external shocks may ....

Medium Term Projection Framework , Economic Outlook , Southeast Asia , Big Data , Large External Shocks , Dsge Model , Natural Disasters , Time Series Analysis , Developing Asia , Covid 19 , நடுத்தர கால ப்ரொஜெக்ஶந் கட்டமைப்பு , பொருளாதார ஔட்‌லுக் , தென்கிழக்கு ஆசியா , பெரியது தகவல்கள் , இயற்கை பேரழிவுகள் , நேரம் தொடர் பகுப்பாய்வு , வளரும் ஆசியா ,

"Liquid" machine-learning system adapts to changing conditions | MIT News | Massachusetts Institute of Technology

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Image: Jose-Luis Olivares, MIT
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MIT researchers have developed a type of neural network that learns on the job, not just during its training phase. These flexible algorithms, dubbed “liquid” networks, change their underlying equations to continuously adapt to new data inputs. The advance could aid decision making based on data streams that change over time, including those involved in medical diagnosis and autonomous driving.
“This is a way forward for the future of robot control, natural language processing, video processing any form of time series data processing,” says Ramin Hasani, the study’s lead author. “The potential is really significant.” ....

Radu Grosu , Ramin Hasani , Daniela Rus , Alexander Amini , Mathias Lechner , Artificial Intelligence Laboratory , Vienna University Of Technology , Technology Austria , Institute Of Science , National Science Foundation , Austrian Science Fund , Erna Viterbi Professor , Electrical Engineering , Computer Science , Vienna University , Electronic Components , Time Series Analysis , Liquid Network , ராமின் ஹசானி , டேனீலா ரஸ் , அலெக்சாண்டர் அமினி , மத்தியாஸ் லெக்நர் , செயற்கை உளவுத்துறை ஆய்வகம் , வியன்னா பல்கலைக்கழகம் ஆஃப் தொழில்நுட்பம் , தொழில்நுட்பம் ஆஸ்ட்ரியா , நிறுவனம் ஆஃப் அறிவியல் ,

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Ticaret Bakanlýðý toplam 110 personel alýmý yapacak
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Ticaret Bakanlığı personel alımı başvurusu nasıl yapılır? Ticaret Bakanlığı uzman yardımcılığı alımı başvuru şartları!

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