in timm 2. Exploring the Limits of Large Scale Pre-training 3. Deep Neural Networks and Tabular Data: A Survey 4. Learning in High Dimension Always Amounts to Extrapolation 5. ADOP: Approximate Differentiable One-Pixel Point Rendering 6. Well-classi fi ed Examples are Underestimated in Classi fi cation with Deep Neural Networks 7. ByteTrack: Multi-Object Tracking by Associating Every Detection Box 8. MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer ← PickUp! 9. Fast Model Editing at Scale 10. Self-supervised Learning is More Robust to Dataset Imbalance
Real numbers, data science and chaos: How to fi t any dataset with a single parameter 3. Delphi: Towards Machine Ethics and Norms 4. Multitask Prompted Training Enables Zero-Shot Task Generalization 5. Nonnegative spatial factorization 6. Learning in High Dimension Always Amounts to Extrapolation 7. StyleAlign: Analysis and Applications of Aligned StyleGAN Models 8. Deep Learning Tools for Audacity: Helping Researchers Expand the Artist's Toolkit 9. ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs 10. Exploring the Limits of Large Scale Pre-training
An improved training procedure in timm 2. Exploring the Limits of Large Scale Pre-training 3. Deep Neural Networks and Tabular Data: A Survey 4. Learning in High Dimension Always Amounts to Extrapolation 5. ADOP: Approximate Differentiable One-Pixel Point Rendering 6. Well-classi fi ed Examples are Underestimated in Classi fi cation with Deep Neural Networks 7. ByteTrack: Multi-Object Tracking by Associating Every Detection Box 8. MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer ← PickUp! 9. Fast Model Editing at Scale 10. Self-supervised Learning is More Robust to Dataset Imbalance
science and chaos: How to fi t any dataset with a single parameter 3. Delphi: Towards Machine Ethics and Norms 4. Multitask Prompted Training Enables Zero-Shot Task Generalization 5. Nonnegative spatial factorization 7. StyleAlign: Analysis and Applications of Aligned StyleGAN Models 8. Deep Learning Tools for Audacity: Helping Researchers Expand the Artist's Toolkit 9. ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
in timm) HeΒʹΑͬͯઃܭ͞ΕͨӨڹྗͷ͋ΔResidual Networksɼଟ͘ͷՊֶจͰۚࣈౝతͳΞʔΩςΫ νϟͱͯ͠औΓ্͛ΒΕ͍ͯ·͢ɽ͜ΕΒͷΞʔΩςΫνϟ௨ৗɺݚڀʹ͓͚ΔσϑΥϧτͷΞʔΩ ςΫνϟͱͯ͠ɺ͋Δ͍৽͍͠ΞʔΩςΫνϟ͕ఏҊ͞ΕͨࡍͷϕʔεϥΠϯͱͯ͠ػೳ͍ͯ͠· ͢ɻ͔͠͠ɺ2015ʹResNetΞʔΩςΫνϟ͕ൃද͞ΕͯҎདྷɺχϡʔϥϧωοτϫʔΫͷτϨʔχ ϯάͷϕετϓϥΫςΟεʹ͍ͭͯେ͖ͳਐల͕͋Γ·ͨ͠ɻ৽ͨͳ࠷దԽˍσʔλΦʔάϝϯςʔ γϣϯʹΑΓɺτϨʔχϯάϨγϐͷ༗ޮੑ͕ߴ·͍ͬͯ·͢ɻຊจͰɺ͜ͷΑ͏ͳਐาΛ౷߹͠ ͨखॱͰτϨʔχϯάͨ͠߹ͷόχϥResNet-50ͷੑೳΛ࠶ධՁ͠·͢ɻզʑɺڝ૪ྗͷ͋Δֶश ઃఆͱࣄલʹֶश͞ΕͨϞσϧΛtimmΦʔϓϯιʔεϥΠϒϥϦͰڞ༗͠ɺকདྷͷݚڀͷͨΊͷΑΓྑ ͍ϕʔεϥΠϯͱཱͯͭ͜͠ͱΛظ͍ͯ͠·͢ɻྫ͑ɺզʑͷΑΓݫֶ͍͠शઃఆͰɺόχϥ ͷResNet-50ɺՃσʔλৠཹͳ͠ͰImageNet-valͷղ૾224x224Ͱ80.4%ͷτοϓ1ਫ਼Λ ୡ͍ͯ͠·͢ɻ·ͨɺҰൠతͳϞσϧʹ͍ͭͯɺզʑͷֶशํ๏ͰಘΒΕͨੑೳΛใࠂ͠·͢ɻ w తɿΞʔΩςΫνϟͷมߋͳ͠ʹɺ3FT/FUͷ࠷ྑͷֶशखॱΛఏڙ͢Δ w Ռɿ1Z5PSDI༻ͷUJNNϥΠϒϥϦͰϞσϧઃఆͱࣄલֶशࡁΈϞσϧΛఏڙ w ํ๏ɿϋΠύʔύϥϝʔλௐ ΫϩεΤϯτϩϐʔଛࣦ͔Βͷ٫ w ݻ༗໊ɿͳ͠ w ஶऀॴଐɿ'BDFCPPL"*3FTFBSDI http://arxiv.org/abs/2110.00476v1
Extrapolation) ิؒͱ֎ૠͷ֓೦ɼਂֶश͔Βؔۙࣅ·Ͱ༷ʑͳͰجຊͱͳΔɽิؒɼ͋ Δαϯϓϧx͕ɼ༩͑ΒΕͨσʔληοτͷತแͷଆ·ͨڥք্ʹ͋Δͱ͖ʹߦΘ ΕΔɽ֎ૠɼx͕ͦͷತแͷ֎ଆʹ͋Δͱ͖ʹߦΘΕΔɽ1ͭͷجຊతͳʢޡͬͨʣೝ ࣝɺ࠷ઌͷΞϧΰϦζϜ͕͏·͘ػೳ͢ΔͷɺֶशσʔλΛਖ਼͘͠ิؒ͢Δೳྗ ͕͋Δ͔Βͩͱ͍͏ͷͰ͋Δɻ2ͭͷޡղɺิؒλεΫσʔληοτશମͰ ߦΘΕΔͱ͍͏ͷͰɺ࣮ࡍɺଟ͘ͷ؍ཧ͕͜ͷԾఆʹґଘ͍ͯ͠Δɻզʑܦ ݧతɺཧతʹ͜ΕΒͷ2ͭͷʹ͠ɺͲΜͳߴ࣍ݩʢ>100ʣͷσʔληοτͰ ɺ΄ͱΜͲ࣮֬ʹิؒى͜Βͳ͍͜ͱΛ࣮ূͨ͠ɻ͜ΕΒͷ݁ՌɺҰൠԽੑೳͷ ࢦඪͱͯ͠ͷݱࡏͷิؒ/֎ૠͷఆٛͷଥੑʹٙΛ͔͚͛ΔͷͰ͋Δɻ http://arxiv.org/abs/2110.09485v1 w తɿߴ࣍ݩۭؒ Ҏ্ ͰσʔληοτͰิ͕ؒى͜Βͳ͍ࣄΛཧతɾܦݧతʹ࣮ূ͢Δɻ w Ռɿ৽͍͠αϯϓϧʹର͢ΔิؒΛҡ࣋͢ΔͨΊʹɺσʔληοτͷαΠζ͕σʔλͷ࣍ݩʹର͠ ͯࢦؔతʹେ͖͘ͳΔ͜ͱΛ࣮ূ͠ɺطଘͷࢦඪΛ൱ఆͨ͠ɻ w ํ๏ɿ࣍ݩ͕૿͑ͨ࣌ʹิؒ͢ΔͨΊͷσʔληοτྔΛཧɾ࣮σʔληοτͷ྆໘Ͱܭࢉ w ݻ༗໊ɿ w ஶऀॴଐɿ'BDFCPPL"*3FTFBSDI
Classi fi cation with Deep Neural Networks) ैདྷͷਂྨϞσϧͷֶशͰɺྨͷѱ͍ྫʹ͠ɺܾఆڥք͔ΒΕͨྨͷྑ͍ྫΛແࢹ͢Δ͜ͱ ͕ৗࣝͰͨ͠ɻྫ͑ɺΫϩεΤϯτϩϐʔଛࣦΛ༻ֶ͍ͯश͢Δ߹ɺΑΓߴ͍Λ࣋ͭྫʢ͢ͳΘͪɺ Α͘ྨ͞ΕͨྫʣɺόοΫϓϩύήʔγϣϯʹ͓͍ͯখ͞ͳޯʹد༩͠·͢ɻ͔͠͠ɺ͜ͷҰൠతͳख๏ ɺදݱֶशɺΤωϧΪʔͷ࠷దԽɺϚʔδϯͷ૿ՃΛ͛Δ͜ͱΛཧతʹ͍ࣔͯ͠·͢ɻ͜ͷܽؕΛଧͪফ ͨ͢Ίʹɺզʑɺྨͷྑ͍ྫʹՃࢉϘʔφεΛ༩͑ͯɺֶशͷߩݙΛ෮׆ͤ͞Δ͜ͱΛఏҊ͢Δɻ͜ͷ ྫɺ͜ΕΒ3ͭͷΛཧతʹղܾ͢ΔͷͰ͋ΔɻຊจͰɺը૾ྨɺάϥϑྨɺػց༁ͳͲ ͷଟ༷ͳλεΫʹ͓͍ͯɺཧతͳ݁ՌΛݕূͨ͠ΓɺຊྫΛ༻͍ͯେ෯ͳੑೳ্Λ࣮ݱ͢Δ͜ͱ Ͱɺ͜ͷओுΛ࣮ূతʹࢧ࣋͢Δɻ͞ΒʹɺຊจɺզʑͷΞΠσΞ͕͜ΕΒ3ͭͷΛղܾͰ͖ΔͨΊɺ ෆۉߧͳྨɺOODݕग़ɺఢରత߈ܸԼͷΞϓϦέʔγϣϯͳͲͷෳࡶͳγφϦΦʹରԠͰ͖Δ͜ͱΛࣔͯ͠ ͍Δɻίʔυɺhttps://github.com/lancopku/well-classi fi ed-examples-are-underestimated ɻ http://arxiv.org/abs/2110.06537v2 w తɾՌɿΫϩεΤϯτϩϐʔϩεΛվྑͨ͠৽͍͠-PTTؔͷఏҊ w ํ๏ɿόοΫϓϩύήʔγϣϯʹΑΔ$SPTT&OUSPQZ $& ϩεͷΛ໌Β͔ʹ͢Δ w ݻ༗໊ɿ&ODPVSBHJOH-PTT &- w ஶऀॴଐɿژେֶ
ഊΛֶͯ͢श࣌ʹݕग़͢Δ͜ͱෆՄೳͰ͋ΔͨΊɺ͜ͷΑ͏ͳϞσϧͷ։ൃऀͱΤϯυϢʔβͷ྆ํ͕ɺϞσϧΛͦͷ· ·ʹͯ͠ෆਖ਼֬ͳग़ྗΛमਖ਼Ͱ͖ΔΑ͏ʹ͢Δ͜ͱ͕·Ε·͢ɻ͔͠͠ɺେنͳχϡʔϥϧωοτϫʔΫֶ͕श͢Δදݱ ࢄ͓ͯ͠ΓɺϒϥοΫϘοΫεԽ͍ͯ͠ΔͨΊɺ͜ͷΑ͏ͳରΛߜͬͨฤूࠔͰ͢ɻͷ͋Δೖྗͱ৽ͨͳر ͷग़ྗ͕1͚ͭͩఏࣔ͞Εͨ߹ɺඍௐΞϓϩʔνΦʔόʔϑΟοτ͢Δ͕͋Δɻ·ͨɺଞͷฤूΞϧΰϦζϜɺඇ ৗʹେ͖ͳϞσϧʹద༻͢Δ߹ɺܭࢉ͕ෆՄೳͰ͋Δ͔ɺ୯ʹޮՌ͕ͳ͍ɻେنͳϞσϧͷϙετϗοΫฤूΛ༰қʹ͢ ΔͨΊʹɺզʑMEND (Model Editor Networks with Gradient Decomposition)ΛఏҊ͠·͢ɻMENDɺඪ४తͳඍௐ ʹΑͬͯಘΒΕͨޯΛɺޯͷϥϯΫղΛ༻͍ͯม͢Δ͜ͱΛֶश͠ɺ͜ͷมͷύϥϝʔλԽΛѻ͍ͯ͘͢͠ ͍·͢ɻMENDɺ100ԯҎ্ͷύϥϝʔλΛ࣋ͭϞσϧͰ͋ͬͯɺ1ͭͷGPU্Ͱ1Ҏʹֶश͢Δ͜ͱ͕Ͱ͖·͢ɻ· ͨɺҰֶशͨ͠MENDɺࣄલʹֶशͨ͠Ϟσϧʹ৽ͨͳฤूΛՃ͑Δ͜ͱ͕Ͱ͖·͢ɻT5ɺGPTɺBERTɺBARTϞσϧΛ ༻͍࣮ͨݧʹΑΓɺMENDɺઍສ͔Β100ԯҎ্ͷύϥϝʔλΛ࣋ͭϞσϧʹରͯ͠ޮՌతͳฤूΛߦ͏͜ͱ͕Ͱ͖Δ། ҰͷϞσϧฤूख๏Ͱ͋Δ͜ͱ͕Θ͔Γ·ͨ͠ɻ࣮ɺhttps://sites.google.com/view/mend-editing ɻ http://arxiv.org/abs/2110.11309v1 w తɿେنͳ5SBOTGPSNFSϞσϧͰͷՃमਖ਼ͷͨΊͷ࠶ֶश࣌ͷʹରॲ͢Δ w ՌɿߴʹɺΦʔόʔϑΟοτͳ͘ඍௐͰ͖ΔϞσϧ.&/%ͷఏҊ w ํ๏ɿϞσϧฤूࣗମΛֶशͱͯ͠ѻ͏ w ݻ༗໊ɿ.&/% IUUQTTJUFTHPPHMFDPNWJFXNFOEFEJUJOH w ஶऀॴଐɿελϯϑΥʔυେֶ
to fi t any dataset with a single parameter) ࣌ܥྻɺը૾ɺԻͳͲɺͲͷΑ͏ͳϞμϦςΟͷσʔλͰ͋ͬͯ ɺ୯Ұͷ࣮ͷύϥϝʔλΛ࣋ͭྑͳεΧϥʔؔʢ࿈ଓɺඍ Մೳ...ʣͰۙࣅͰ͖Δ͜ͱΛࣔ͠·͢ɻຊݚڀͰɺΧΦεཧͷ جຊతͳ֓೦ʹج͍ͮͯɺσʔλͷͯ͢ͷαϯϓϧʹҙͷਫ਼Ͱ ϑΟοτͤ͞ΔͨΊʹɺ͜ͷύϥϝʔλΛௐ͢Δํ๏Λࣔ͢ڭҭత ͳΞϓϩʔνΛ࠾༻͍ͯ͠·͢ɻح৺ԢͳσʔλαΠΤϯςΟε τΛରʹɺػցֶशϞσϧͷදݱྗͱҰൠԽʹؔ͢Δ͜Ε·Ͱͷಉ ༷ͷ؍݁ՌΛൃలͤͨ͞ͷͰ͢ɻ http://arxiv.org/abs/1904.12320v1 w తɿҙͷσʔληοτ9ͷͯ͢ͷαϯϓϧ͕ɼ୯७ͳඍํఔࣜʹΑͬͯ࠶ݱͰ͖Δ͜ͱΛࣔ͢͜ͱ w ՌɿҰͭͷ࣮ύϥϝʔλ͚ͩͰશͯͷԻɾࢹ֮σʔλΛੜͰ͖Δɺ୯७ͰඍՄೳͳఆࣜԽ w ํ๏ɿΧΦεཧΛݩʹͨ͠Իɾը૾σʔλͷҰൠԽ w ݻ༗໊ɿ4JOHMF1BSBNFUFS'JU IUUQTHJUIVCDPN3BOMPUTJOHMFQBSBNFUFS fi U w ஶऀॴଐɿ4"1-BCT υΠπͷιϑτΣΞاۀͷݚڀػؔ
ͦͷ࣮જࡏҼࢠͱෛՙྔղऍ͕͍͠ɻຊݚڀͰɺۭؒΛߟྀͨ֬͠త࣍ݩݮϞσϧͰ͋Δ ඇෛͷۭؒҼࢠԽʢNSFʣΛఏҊ͢ΔɻNSFɺγϛϡϨʔγϣϯͱߴ࣍ݩۭؒτϥϯεΫϦϓτϛΫ εσʔλΛ༻͍ͯɺMEFISTOͷΑ͏ͳ࣮ۭؒҼࢠԽඇۭؒ࣍ݩݮ๏ͱൺֱͨ͠ɻNSFɺҨࢠ ൃݱͷҰൠԽՄೳͳۭؒύλʔϯΛಛఆ͠·͢ɻͯ͢ͷҨࢠൃݱύλʔϯ͕ۭؒతͰ͋ΔͱݶΒ ͳ͍ͨΊɺۭؒతͳཁૉͱඇۭؒతͳཁૉΛΈ߹ΘͤͨNSFͷϋΠϒϦου֦ுΛఏҊ͠ɺ؍ଌͱ ಛͷ྆ํʹ͍ۭͭͯؒతͳॏཁੑΛఆྔԽ͢Δ͜ͱΛՄೳʹ͍ͯ͠·͢ɻNSFͷTensorFlow࣮ɺ https://github.com/willtownes/nsf-paper ͔ΒೖखՄೳͰ͋Δɻ http://arxiv.org/abs/2110.06122v1 w తɿੜମ৫ͷݚڀʹ͓͚ΔۭؒతͳҨࢠൃݱͷଌఆ w ՌɿҨࢠൃݱͷҰൠԽՄೳͳۭؒύλʔϯΛಛఆ w ํ๏ɿΨεաఔΛ༻͍ͨσʔλղੳͰͷۭؒΛߟྀͨ֬͠త࣍ݩݮϞσϧΛߟҊ w ݻ༗໊ɿ/4' /POOFHBUJWF4QBUJBM'BDUPSJ[BUJPO /4') /4')ZCSJE w ஶऀॴଐɿϓϦϯετϯେֶ άϥουετʔϯݚڀॴ αϯϑϥϯγεί
Expand the Artist's Toolkit) ࢲͨͪɺΦʔϓϯιʔεͷਓؾΦʔσΟΦฤूιϑτAudacity ʹɺ࠷খݶͷ։ൃऀͷ࿑ྗͰχϡʔϥϧωοτϫʔΫΛ౷߹͢Δι ϑτΣΞϑϨʔϜϫʔΫΛհ͠·͢ɻຊจͰɺΤϯυϢʔ βʔͱχϡʔϥϧωοτϫʔΫ։ൃऀͷ྆ํʹ͚ͯɺ͍͔ͭ͘ͷ ༻ྫΛհ͠·͢ɻ͜ͷݚڀ͕ɺਂֶशͷ࣮ફऀͱΤϯυϢʔ βʔͷؒͷ৽͍͠Ϩϕϧͷ૬ޓ࡞༻Λଅਐ͢Δ͜ͱΛظ͍ͯ͠· ͢ɻ http://arxiv.org/abs/2110.13323v1 w తɾՌɿԻฤूιϑτ"VEBDJUZΛχϡʔϥϧωοτϫʔΫʹରԠ͢Δ w ํ๏ɿΦϯϥΠϯαΠτ)VHHJOH'BDFͷެ։ϞσϧʹରԠ w ݻ༗໊ɿ"VEBDJUZ%JHJUBM"VEJP8PSLTUBUJPO w ஶऀॴଐɿϊʔεΣελϯେֶ "VEBDJUZ5FBN Իฤूιϑτ։ൃνʔϜ
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