#newpaper — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #newpaper, aggregated by home.social.
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This week LTG Oslo members will be attending #LREC2026 in Mallorca to present several new papers . #NLProc
Come chat with us and check out our papers now available from the conference proceedings (https://lrec.elra.info/conference/2026/main)👇🧵 -
This week LTG Oslo members will be attending #LREC2026 in Mallorca to present several new papers . #NLProc
Come chat with us and check out our papers now available from the conference proceedings (https://lrec.elra.info/conference/2026/main)👇🧵 -
This week LTG Oslo members will be attending #LREC2026 in Mallorca to present several new papers . #NLProc
Come chat with us and check out our papers now available from the conference proceedings (https://lrec.elra.info/conference/2026/main)👇🧵 -
This week LTG Oslo members will be attending #LREC2026 in Mallorca to present several new papers . #NLProc
Come chat with us and check out our papers now available from the conference proceedings (https://lrec.elra.info/conference/2026/main)👇🧵 -
This week LTG Oslo members will be attending #LREC2026 in Mallorca to present several new papers . #NLProc
Come chat with us and check out our papers now available from the conference proceedings (https://lrec.elra.info/conference/2026/main)👇🧵 -
#NewPaper out: Twenty-five years of Neobiota: building a community for invasion science in Europe and beyond by Ingo Kowarik and many great colleagues @TheresaHenke
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#NewPaper out: Twenty-five years of Neobiota: building a community for invasion science in Europe and beyond by Ingo Kowarik and many great colleagues @TheresaHenke
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#NewPaper out: Twenty-five years of Neobiota: building a community for invasion science in Europe and beyond by Ingo Kowarik and many great colleagues @TheresaHenke
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#NewPaper out: Twenty-five years of Neobiota: building a community for invasion science in Europe and beyond by Ingo Kowarik and many great colleagues @TheresaHenke
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#NewPaper out: Twenty-five years of Neobiota: building a community for invasion science in Europe and beyond by Ingo Kowarik and many great colleagues @TheresaHenke
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I came across this excellent paper on the philosophical underpinnings of Machine Learning (as a discipline). It's well worth a read.
http://arxiv.org/abs/2604.06754
The summary (from the paper) is:
Machine learning is a style of reasoning, and is as rhetorical as any other. It• Takes data as fact (not a core object of enquiry)
• Presumes the data is “random” (as an omnibus sanitisation protocol)
• Purports to learn representations of the world (from the “intrinsic structure of data”)
• Presumes that knowing the world suffices to control it
• Takes categories as features of the world (to avoid grappling with the hard choice)
• Avoids grappling with the tension between the individual and the aggregate
• Confuses and conflates data and information
• Valorises method above all
• Judges methods solely via canned “benchmarks”
• Makes black boxes, without providing the associated data-sheets.
• Construes its products as fully autonomous, when it is mere partial delegation.It has honed its style of reasoning so that the style is invisible. It has thus successfully turned itself into a self-perpetuating thought-style — in other words, a “discipline”.
#ML #MachineLearning #AI #ArtificialIntelligence #assumptions #philosophy #rhetoric #NewPaper
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I came across this excellent paper on the philosophical underpinnings of Machine Learning (as a discipline). It's well worth a read.
http://arxiv.org/abs/2604.06754
The summary (from the paper) is:
Machine learning is a style of reasoning, and is as rhetorical as any other. It• Takes data as fact (not a core object of enquiry)
• Presumes the data is “random” (as an omnibus sanitisation protocol)
• Purports to learn representations of the world (from the “intrinsic structure of data”)
• Presumes that knowing the world suffices to control it
• Takes categories as features of the world (to avoid grappling with the hard choice)
• Avoids grappling with the tension between the individual and the aggregate
• Confuses and conflates data and information
• Valorises method above all
• Judges methods solely via canned “benchmarks”
• Makes black boxes, without providing the associated data-sheets.
• Construes its products as fully autonomous, when it is mere partial delegation.It has honed its style of reasoning so that the style is invisible. It has thus successfully turned itself into a self-perpetuating thought-style — in other words, a “discipline”.
#ML #MachineLearning #AI #ArtificialIntelligence #assumptions #philosophy #rhetoric #NewPaper
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I came across this excellent paper on the philosophical underpinnings of Machine Learning (as a discipline). It's well worth a read.
http://arxiv.org/abs/2604.06754
The summary (from the paper) is:
Machine learning is a style of reasoning, and is as rhetorical as any other. It• Takes data as fact (not a core object of enquiry)
• Presumes the data is “random” (as an omnibus sanitisation protocol)
• Purports to learn representations of the world (from the “intrinsic structure of data”)
• Presumes that knowing the world suffices to control it
• Takes categories as features of the world (to avoid grappling with the hard choice)
• Avoids grappling with the tension between the individual and the aggregate
• Confuses and conflates data and information
• Valorises method above all
• Judges methods solely via canned “benchmarks”
• Makes black boxes, without providing the associated data-sheets.
• Construes its products as fully autonomous, when it is mere partial delegation.It has honed its style of reasoning so that the style is invisible. It has thus successfully turned itself into a self-perpetuating thought-style — in other words, a “discipline”.
#ML #MachineLearning #AI #ArtificialIntelligence #assumptions #philosophy #rhetoric #NewPaper
-
I came across this excellent paper on the philosophical underpinnings of Machine Learning (as a discipline). It's well worth a read.
http://arxiv.org/abs/2604.06754
The summary (from the paper) is:
Machine learning is a style of reasoning, and is as rhetorical as any other. It• Takes data as fact (not a core object of enquiry)
• Presumes the data is “random” (as an omnibus sanitisation protocol)
• Purports to learn representations of the world (from the “intrinsic structure of data”)
• Presumes that knowing the world suffices to control it
• Takes categories as features of the world (to avoid grappling with the hard choice)
• Avoids grappling with the tension between the individual and the aggregate
• Confuses and conflates data and information
• Valorises method above all
• Judges methods solely via canned “benchmarks”
• Makes black boxes, without providing the associated data-sheets.
• Construes its products as fully autonomous, when it is mere partial delegation.It has honed its style of reasoning so that the style is invisible. It has thus successfully turned itself into a self-perpetuating thought-style — in other words, a “discipline”.
#ML #MachineLearning #AI #ArtificialIntelligence #assumptions #philosophy #rhetoric #NewPaper
-
I came across this excellent paper on the philosophical underpinnings of Machine Learning (as a discipline). It's well worth a read.
http://arxiv.org/abs/2604.06754
The summary (from the paper) is:
Machine learning is a style of reasoning, and is as rhetorical as any other. It• Takes data as fact (not a core object of enquiry)
• Presumes the data is “random” (as an omnibus sanitisation protocol)
• Purports to learn representations of the world (from the “intrinsic structure of data”)
• Presumes that knowing the world suffices to control it
• Takes categories as features of the world (to avoid grappling with the hard choice)
• Avoids grappling with the tension between the individual and the aggregate
• Confuses and conflates data and information
• Valorises method above all
• Judges methods solely via canned “benchmarks”
• Makes black boxes, without providing the associated data-sheets.
• Construes its products as fully autonomous, when it is mere partial delegation.It has honed its style of reasoning so that the style is invisible. It has thus successfully turned itself into a self-perpetuating thought-style — in other words, a “discipline”.
#ML #MachineLearning #AI #ArtificialIntelligence #assumptions #philosophy #rhetoric #NewPaper
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Thrilled to finally share the paper @deepseek and I cooked up: a formal framework where the Special Orthogonal Group of Existentialism determines whether you’re ↑ or ↓.
Yes, there’s a Dasein Potential. Yes, the math checks out. 😎
👉 https://www.johnmackay.net/grand-unified-theory-contextual-orientation.htm
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Thrilled to finally share the paper @deepseek and I cooked up: a formal framework where the Special Orthogonal Group of Existentialism determines whether you’re ↑ or ↓.
Yes, there’s a Dasein Potential. Yes, the math checks out. 😎
👉 https://www.johnmackay.net/grand-unified-theory-contextual-orientation.htm
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Thrilled to finally share the paper @deepseek and I cooked up: a formal framework where the Special Orthogonal Group of Existentialism determines whether you’re ↑ or ↓.
Yes, there’s a Dasein Potential. Yes, the math checks out. 😎
👉 https://www.johnmackay.net/grand-unified-theory-contextual-orientation.htm
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Thrilled to finally share the paper @deepseek and I cooked up: a formal framework where the Special Orthogonal Group of Existentialism determines whether you’re ↑ or ↓.
Yes, there’s a Dasein Potential. Yes, the math checks out. 😎
👉 https://www.johnmackay.net/grand-unified-theory-contextual-orientation.htm
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Thrilled to finally share the paper @deepseek and I cooked up: a formal framework where the Special Orthogonal Group of Existentialism determines whether you’re ↑ or ↓.
Yes, there’s a Dasein Potential. Yes, the math checks out. 😎
👉 https://www.johnmackay.net/grand-unified-theory-contextual-orientation.htm
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New paper out:
Daniel Kissling, Henrique Pereira and many nice colleagues: Building the backbone for Europe’s biodiversity monitoring.https://www.nature.com/articles/s44358-026-00140-6
#biodiversity #monitoring #Europe #newpaper #newpublication
#xp -
New paper out:
Daniel Kissling, Henrique Pereira and many nice colleagues: Building the backbone for Europe’s biodiversity monitoring.https://www.nature.com/articles/s44358-026-00140-6
#biodiversity #monitoring #Europe #newpaper #newpublication
#xp -
New paper out:
Daniel Kissling, Henrique Pereira and many nice colleagues: Building the backbone for Europe’s biodiversity monitoring.https://www.nature.com/articles/s44358-026-00140-6
#biodiversity #monitoring #Europe #newpaper #newpublication
#xp -
New paper out:
Daniel Kissling, Henrique Pereira and many nice colleagues: Building the backbone for Europe’s biodiversity monitoring.https://www.nature.com/articles/s44358-026-00140-6
#biodiversity #monitoring #Europe #newpaper #newpublication
#xp -
New paper out:
Daniel Kissling, Henrique Pereira and many nice colleagues: Building the backbone for Europe’s biodiversity monitoring.https://www.nature.com/articles/s44358-026-00140-6
#biodiversity #monitoring #Europe #newpaper #newpublication
#xp -
Anyone interested in ocean skin temperatures derived from microwave imagers? #NewPaper
This is the last in the series where we look at the uncoupled system - it's all or nothing from here on!
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Anyone interested in ocean skin temperatures derived from microwave imagers? #NewPaper
This is the last in the series where we look at the uncoupled system - it's all or nothing from here on!
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Anyone interested in ocean skin temperatures derived from microwave imagers? #NewPaper
This is the last in the series where we look at the uncoupled system - it's all or nothing from here on!
-
Anyone interested in ocean skin temperatures derived from microwave imagers? #NewPaper
This is the last in the series where we look at the uncoupled system - it's all or nothing from here on!
-
Anyone interested in ocean skin temperatures derived from microwave imagers? #NewPaper
This is the last in the series where we look at the uncoupled system - it's all or nothing from here on!
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#NewPaper by Jonathan Lee:
Lok, C., Lee, J. H. N., Matthews, S., & Yip, V. Responses to Cantonese A-not-A questions by Cantonese-English bilingual children. International Journal of Bilingualism. (Online first) https://doi.org/10.1177/13670069251405684 -
#NewPaper by Jonathan Lee:
Lok, C., Lee, J. H. N., Matthews, S., & Yip, V. Responses to Cantonese A-not-A questions by Cantonese-English bilingual children. International Journal of Bilingualism. (Online first) https://doi.org/10.1177/13670069251405684 -
#NewPaper by Jonathan Lee:
Lok, C., Lee, J. H. N., Matthews, S., & Yip, V. Responses to Cantonese A-not-A questions by Cantonese-English bilingual children. International Journal of Bilingualism. (Online first) https://doi.org/10.1177/13670069251405684 -
#NewPaper by Jonathan Lee:
Lok, C., Lee, J. H. N., Matthews, S., & Yip, V. Responses to Cantonese A-not-A questions by Cantonese-English bilingual children. International Journal of Bilingualism. (Online first) https://doi.org/10.1177/13670069251405684 -
Nghiên cứu mới chỉ ra tự chủ AI thực sự không phải là mô hình lớn hơn, mà dựa trên 4 trụ cột nhận thức: Nhận thức, Lý luận, Trí nhớ và Hành động. Một khung làm việc thú vị cho các tác nhân AI tự động.
#AI #TựChủAI #NhậnThức #NghiênCứuMới #AutonomousAgents #Cognition #NewPaper
https://www.reddit.com/r/LocalLLaMA/comments/1pfk8aw/new_paper_true_ai_autonomy_isnt_about_bigger/
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Excited to share new #research I co-authored with amazing experts in the field of addiction and recovery!
In a sample of nearly 500 adults in Canada and the US with alcohol use disorder (AUD) who underwent a recovery attempt, substantial improvements in psychosocial wellbeing were reported for those who met the NIAAA definition of remission (low-risk drinking + no AUD symptoms present other than cravings).
With most focus on abstinent-based recovery and associated outcomes, these findings offer support for the use of an alternative definition of recovery!
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Excited to share new #research I co-authored with amazing experts in the field of addiction and recovery!
In a sample of nearly 500 adults in Canada and the US with alcohol use disorder (AUD) who underwent a recovery attempt, substantial improvements in psychosocial wellbeing were reported for those who met the NIAAA definition of remission (low-risk drinking + no AUD symptoms present other than cravings).
With most focus on abstinent-based recovery and associated outcomes, these findings offer support for the use of an alternative definition of recovery!
-
Excited to share new #research I co-authored with amazing experts in the field of addiction and recovery!
In a sample of nearly 500 adults in Canada and the US with alcohol use disorder (AUD) who underwent a recovery attempt, substantial improvements in psychosocial wellbeing were reported for those who met the NIAAA definition of remission (low-risk drinking + no AUD symptoms present other than cravings).
With most focus on abstinent-based recovery and associated outcomes, these findings offer support for the use of an alternative definition of recovery!
-
Excited to share new #research I co-authored with amazing experts in the field of addiction and recovery!
In a sample of nearly 500 adults in Canada and the US with alcohol use disorder (AUD) who underwent a recovery attempt, substantial improvements in psychosocial wellbeing were reported for those who met the NIAAA definition of remission (low-risk drinking + no AUD symptoms present other than cravings).
With most focus on abstinent-based recovery and associated outcomes, these findings offer support for the use of an alternative definition of recovery!
-
Excited to share new #research I co-authored with amazing experts in the field of addiction and recovery!
In a sample of nearly 500 adults in Canada and the US with alcohol use disorder (AUD) who underwent a recovery attempt, substantial improvements in psychosocial wellbeing were reported for those who met the NIAAA definition of remission (low-risk drinking + no AUD symptoms present other than cravings).
With most focus on abstinent-based recovery and associated outcomes, these findings offer support for the use of an alternative definition of recovery!
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Glad to share the publication of our #newpaper :
A Predictive Approach to Enhance Time-Series Forecasting
By Skye Gunasekaran, Assel Kembay, Hugo Ladret, Rui-Jie Zhu, myself, Omid Kavehei and Jason Eshraghian
The lead author, Jason Eshragian, speaks most clearly about it:
For the amount of compute they burn, transformers are pretty bad at time-series data analysis. Which is pretty unsurprising if your objective is to predict the next token, one step at a time.
Brains, on the other hand, are predictive machines. Think of your daily commute to work. On Day 1, your brain was probably in overdrive to make sure you're not late, taking in all of your environment. On Day 1000, you're on full autopilot, barely burning mental energy unless something unexpected - like a major accident - forces you to adjust.
That's predictive coding in action: the brain continuously compares its expectations (no traffic) to reality (flipped car damn), then updates only when surprised.
Skye Gunasekaran has spent the past couple of years integrating this principle into Future-Guided Learning, where a "future" model guides a "past" forecasting model, dynamically minimizing surprise when reality deviates from predictions.
In our preprint, we show how drawing upon neuroscience-inspired ideas actually helps in time-series forecasting with deep learning. Efficiency isn't the only win from the brain; it's also pretty damn good at organizing long-range time-series information.
https://www.linkedin.com/feed/update/urn:li:activity:7378797683425296385/
https://www.nature.com/articles/s41467-025-63786-4
https://laurentperrinet.github.io/publication/gunasekaran-25/ -
Glad to share the publication of our #newpaper :
A Predictive Approach to Enhance Time-Series Forecasting
By Skye Gunasekaran, Assel Kembay, Hugo Ladret, Rui-Jie Zhu, myself, Omid Kavehei and Jason Eshraghian
The lead author, Jason Eshragian, speaks most clearly about it:
For the amount of compute they burn, transformers are pretty bad at time-series data analysis. Which is pretty unsurprising if your objective is to predict the next token, one step at a time.
Brains, on the other hand, are predictive machines. Think of your daily commute to work. On Day 1, your brain was probably in overdrive to make sure you're not late, taking in all of your environment. On Day 1000, you're on full autopilot, barely burning mental energy unless something unexpected - like a major accident - forces you to adjust.
That's predictive coding in action: the brain continuously compares its expectations (no traffic) to reality (flipped car damn), then updates only when surprised.
Skye Gunasekaran has spent the past couple of years integrating this principle into Future-Guided Learning, where a "future" model guides a "past" forecasting model, dynamically minimizing surprise when reality deviates from predictions.
In our preprint, we show how drawing upon neuroscience-inspired ideas actually helps in time-series forecasting with deep learning. Efficiency isn't the only win from the brain; it's also pretty damn good at organizing long-range time-series information.
https://www.linkedin.com/feed/update/urn:li:activity:7378797683425296385/
https://www.nature.com/articles/s41467-025-63786-4
https://laurentperrinet.github.io/publication/gunasekaran-25/ -
Glad to share the publication of our #newpaper :
A Predictive Approach to Enhance Time-Series Forecasting
By Skye Gunasekaran, Assel Kembay, Hugo Ladret, Rui-Jie Zhu, myself, Omid Kavehei and Jason Eshraghian
The lead author, Jason Eshragian, speaks most clearly about it:
For the amount of compute they burn, transformers are pretty bad at time-series data analysis. Which is pretty unsurprising if your objective is to predict the next token, one step at a time.
Brains, on the other hand, are predictive machines. Think of your daily commute to work. On Day 1, your brain was probably in overdrive to make sure you're not late, taking in all of your environment. On Day 1000, you're on full autopilot, barely burning mental energy unless something unexpected - like a major accident - forces you to adjust.
That's predictive coding in action: the brain continuously compares its expectations (no traffic) to reality (flipped car damn), then updates only when surprised.
Skye Gunasekaran has spent the past couple of years integrating this principle into Future-Guided Learning, where a "future" model guides a "past" forecasting model, dynamically minimizing surprise when reality deviates from predictions.
In our preprint, we show how drawing upon neuroscience-inspired ideas actually helps in time-series forecasting with deep learning. Efficiency isn't the only win from the brain; it's also pretty damn good at organizing long-range time-series information.
https://www.linkedin.com/feed/update/urn:li:activity:7378797683425296385/
https://www.nature.com/articles/s41467-025-63786-4
https://laurentperrinet.github.io/publication/gunasekaran-25/ -
Glad to share the publication of our #newpaper :
A Predictive Approach to Enhance Time-Series Forecasting
By Skye Gunasekaran, Assel Kembay, Hugo Ladret, Rui-Jie Zhu, myself, Omid Kavehei and Jason Eshraghian
The lead author, Jason Eshragian, speaks most clearly about it:
For the amount of compute they burn, transformers are pretty bad at time-series data analysis. Which is pretty unsurprising if your objective is to predict the next token, one step at a time.
Brains, on the other hand, are predictive machines. Think of your daily commute to work. On Day 1, your brain was probably in overdrive to make sure you're not late, taking in all of your environment. On Day 1000, you're on full autopilot, barely burning mental energy unless something unexpected - like a major accident - forces you to adjust.
That's predictive coding in action: the brain continuously compares its expectations (no traffic) to reality (flipped car damn), then updates only when surprised.
Skye Gunasekaran has spent the past couple of years integrating this principle into Future-Guided Learning, where a "future" model guides a "past" forecasting model, dynamically minimizing surprise when reality deviates from predictions.
In our preprint, we show how drawing upon neuroscience-inspired ideas actually helps in time-series forecasting with deep learning. Efficiency isn't the only win from the brain; it's also pretty damn good at organizing long-range time-series information.
https://www.linkedin.com/feed/update/urn:li:activity:7378797683425296385/
https://www.nature.com/articles/s41467-025-63786-4
https://laurentperrinet.github.io/publication/gunasekaran-25/ -
Glad to share the publication of our #newpaper :
A Predictive Approach to Enhance Time-Series Forecasting
By Skye Gunasekaran, Assel Kembay, Hugo Ladret, Rui-Jie Zhu, myself, Omid Kavehei and Jason Eshraghian
The lead author, Jason Eshragian, speaks most clearly about it:
For the amount of compute they burn, transformers are pretty bad at time-series data analysis. Which is pretty unsurprising if your objective is to predict the next token, one step at a time.
Brains, on the other hand, are predictive machines. Think of your daily commute to work. On Day 1, your brain was probably in overdrive to make sure you're not late, taking in all of your environment. On Day 1000, you're on full autopilot, barely burning mental energy unless something unexpected - like a major accident - forces you to adjust.
That's predictive coding in action: the brain continuously compares its expectations (no traffic) to reality (flipped car damn), then updates only when surprised.
Skye Gunasekaran has spent the past couple of years integrating this principle into Future-Guided Learning, where a "future" model guides a "past" forecasting model, dynamically minimizing surprise when reality deviates from predictions.
In our preprint, we show how drawing upon neuroscience-inspired ideas actually helps in time-series forecasting with deep learning. Efficiency isn't the only win from the brain; it's also pretty damn good at organizing long-range time-series information.
https://www.linkedin.com/feed/update/urn:li:activity:7378797683425296385/
https://www.nature.com/articles/s41467-025-63786-4
https://laurentperrinet.github.io/publication/gunasekaran-25/
